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Artificial Intelligence, Employment, and Income | AI Magazine
|
Artificial Intelligence, Employment, and Income
|
https://ojs.aaai.org
|
[
"Nils J. Nilsson"
] |
In this article we explore how AI is likely to affect employment and the distribution of income. We argue that AI will indeed reduce drastically the need of ...
|
Authors Nils J. Nilsson
Abstract Artificial intelligence (AI) will have profound societal effects. It promises potential benefits (and may also pose risks) in education, defense, business, law and science. In this article we explore how AI is likely to affect employment and the distribution of income. We argue that AI will indeed reduce drastically the need of human toil. We also note that some people fear the automation of work by machines and the resulting of unemployment. Yet, since the majority of us probably would rather use our time for activities other than our present jobs, we ought thus to greet the work-eliminating consequences of AI enthusiastically. The paper discusses two reasons, one economic and one psychological, for this paradoxical apprehension. We conclude with discussion of problems of moving toward the kind of economy that will be enabled by developments in AI.
| 1984-06-15T00:00:00 |
https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/433
|
[
{
"date": "1984/06/15",
"position": 44,
"query": "artificial intelligence employment"
},
{
"date": "1984/06/15",
"position": 45,
"query": "artificial intelligence employment"
}
] |
|
Views from Those Who Expect AI and Robotics to Displace More ...
|
Views from Those Who Expect AI and Robotics to Displace More Jobs than They Create by 2025
|
https://www.pewresearch.org
|
[
"Aaron Smith",
"Janna Anderson",
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"Font-Family Var --Wp--Preset--Font-Family--Sans-Serif",
"Font-Size Font-Weight Gap Important Line-Height",
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] |
People will be displaced, businesses will slowly transition their older workforce to different jobs and not hire younger people or veterans.
|
[will]
Advances in technology will absolutely reduce human jobs—this process is already underway, and the logic of our economy and technological advancement make it a sure thing to continue
Many of the experts in our survey who expect technology to be a net job destroyer also looked to the history of technology and employment in making their case. But in contrast to the group discussed above, these respondents see a much different story—one in which advances in automation have been taking jobs and putting downward pressure on wage growth for years.
Alex Halavais, associate professor of social and behavioral sciences at Arizona State University, predicted, “They have probably already replaced more jobs than they have created. The slow recovery in the U.S. is closely tied to our worker productivity, which is in turn related to our use of technology. We are only at the cusp of this, and I suspect it will be far more obvious and pronounced by 2025. I suspect that ATMs and self-checkout are just the starting points. The biggest shift will be fairly invisible in the next 10 years, because they will be in manufacturing, particularly at small scale. Tesla’s factory is new standard for relatively small-scale production. As some kinds of standardized service jobs become more easily addressed by scalable technology, they will go the way of phone operators and bank tellers. That is, they will not disappear entirely, but they will be radically reduced. Right now, things like food service, travel, and hospitality are being kept human for cultural rather than purely economic reasons, I suspect.”
Larry Gell, founder and director of the International Agency for Economic Development (IAED), responded, “After 50+ years working for the heads of the world’s biggest corporations all over the globe—watching them cut costs every place starting with the biggest cost: PEOPLE; moving labor to cheapest markets, then replacing them as fast as possible with robots and automation—why would it stop? It will accelerate. Anything and everything that can be automated to replace humans will be done. You can bet on it!”
Mary Joyce, an Internet researcher and digital activism consultant, replied, “To the extent that human workers can be replaced by robots and algorithms, they will be. There’s no reason to believe that firms would behave in any other ways. And social forces, like unions, that would limit these actions, don’t have the strength to prevent these changes.”
Karl Fogel, a partner with Open Tech Strategies and president of QuestionCopyright.org, wrote, “The reason people are investing in machine agents is precisely that they will replace more (lower-paid) humans than the number of (more highly-paid) humans needed to build and maintain the machines. But this is not a new phenomenon—it’s been going on for more than a century. We’re going to have to come to grips with a long-term employment crisis and the fact that—strictly from an economic point of view, not a moral point of view—there are more and more ‘surplus humans.’”
John Wilbanks, chief commons officer for Sage Bionetworks, wrote, “There remain enormous market gaps where digital tools can replace people, from parking lot attendants to call centers to checkout lanes in retail. Those jobs will go and won’t come back.”
A distinguished engineer working in networking for Dell wrote, “It’s a given that computers will get more powerful and be able to perform more and more intelligent tasks. This is going to create more unemployment and I’m not sure how all this gets resolved.”
Dean Thrasher, founder of Infovark, Inc., wrote, “More and more fields seem ripe for automation, but it’s hard to think of areas of our economy that are suffering from lack of staff—possibly teaching or healthcare? Yet we are applying more robotics and AI in these fields as well. I think technology’s negative impact on employment is likely to grow worse in the near future, rather than better. It’s easier to think of the few areas that will be resistant to robotics: sports leagues, symphony orchestras, craft brewing, ballet, and fine art. If the human touch is not essential to the task, it’s fair to assume that it could be automated away.”
Bernard Glassman wrote, “I’m honestly trying to think about the last time I heard anyone of any importance argue with a straight face that we should adopt a new technology because it will create jobs. At best, new robotics technologies move people into the service sector, at least until the service itself can be automated. Take 3-D printing—can we honestly believe that it will generate more high-level jobs than it kills?”
Lyman Chapin, co-founder and principal of Interisle Consulting Group, wrote, “Anything that can be automated will be, and to a greater or lesser extent depending on circumstances businesses will be reluctant to hire people to perform tasks that can be performed by robots, digital agents, or AI applications.”
Dave Kissoondoyal, CEO for KMP Global Ltd. and Internet consultant active in Internet governance activities, wrote, “We have already witnessed the effects of mechanization and automation on the labor force. Similarly—networked, automated, artificial intelligence (AI) applications and robotic devices will have displaced more jobs than they have created by 2025. The effects this time will be for both white and blue-collar jobs.”
Mark Johnson, CTO and vice president for architecture at MCNC wrote, “The trend towards automation of every job seems inexorable. This probably has a disproportionate effect on older workers of all kinds who are less agile in their ability to move about in the economy than younger workers.”
Serge Marelli, a past member of IEEE and ACM, wrote, “Automated cars can cheaply replace public transportation drivers, and automated cleaners and caregivers might very well replace human-help and caretakers for the sick and elderly. While this may seem in the short term more economic, it will be a fatal blow to the last local jobs for those with less skills (formal education).”
These advances are different from what has come before them—the changes are more rapid, and are going to impact people and professions that have thus far been insulated from automation
Many respondents worry that the current wave of technological change is going to impact previously insulated professions, and will happen so quickly as to prevent people from adjusting to new career paths.
Jeremy Epstein, a senior computer scientist at SRI International, responded, “The net number of jobs displaced will be fairly small, but they will be disproportionately blue-collar and pink-collar jobs going away and new white-collar jobs created. Just as travel agents (a pink collar job) have been largely replaced by Kayak and the like, many other service jobs like taxi drivers will largely disappear. There are no elevator operators left in the Western world (I’ve seen them still in India, though), why would anyone need a human to pilot a car to a location? Having a human driver may be seen as a status symbol for the wealthy, but even they will see the value in not having to worry about their driver’s sobriety or willingness to share overheard secrets. Blue-collar jobs like construction will still exist, because the costs of automation are too high. However, even they will be reduced as there’s more factory-built housing, which allows for cost effective use of robots in the construction process. It’s hard for me to guess how things like garbage collecting will be affected—use of equipment has reduced the number of people involved, and self-driving vehicles could reduce it further, but given the low wages it might not be worth eliminating people altogether.”
Joel Halpern, a distinguished engineer at Ericsson, wrote, “While the advent of automated assistive technology will enable many new jobs, it will likely render irrelevant many current jobs. I expect that in the same time frame other technologies will likely create many opportunities, but in terms of the direct job destruction, creation, and disruption from automated operational technologies such as implied by the question will likely be negative in terms of numbers of jobs. While the effect will be felt more on the ‘blue-collar’ level, it will likely also occur at the ‘white-collar’ level as well.”
An attorney at a major law firm responded, “The field within which I work currently employs many thousands to review documents. They are already being replaced by predictive coding algorithms. By 2025, those jobs will not exist for any but the most opaque documents and thus there will be many thousands of lawyers out of work. I find it difficult to imagine any industry which is more knowledge and thought intensive than law and we are already being replaced by machines. I suspect this will disrupt most industries.”
David Allen, an academic and advocate engaged with the development of global Internet governance, replied, “The underlying, fundamental determinant is rate of change, between invention and the workforce. The last century plus has seen the most phenomenal acceleration in the rate of change for innovation. The rate seems likely to continue high. On the other hand, people change and adapt to these changes in the real world only with difficulty. If this is correct, then the rate of change in invention will continue to overwhelm the ability of people—in this case the workforce—to adjust to that change.”
The CEO of a company that makes intelligent machines to make you smarter about your money wrote, “Most information work isn’t all that complicated. Rarely, in fact, does it require the kind of creative manipulation of symbols that usually counts for human intelligence. Where such tasks can be automated, they will be with an appropriate reduction in the human effort required. We’re just seeing the first fruits of this automation today, in fields like banking, where traditional retail banking services have been reduced to a couple of clicks in a mobile application—who needs a branch teller when you can have that teller in your pocket? This goes double for truly mundane tasks like securities trading, where algorithms running in server farms located in the same co-lo as the exchanges execute 50% of a day’s trades on many markets. Where the money goes, so goes the society. I expect the service industries will survive for another 50 years or so past 2025, but then they will be ripe for automation as well, once we can build computers that can process natural human language more accurately, and robots that can simulate human behavior more closely.”
A university professor from the United States wrote, “The impact of AIs and robotics is often, I think, overstated, but automation of vehicles and improvements in robotics in warehousing operations should lead to a steady loss of employment in all areas of logistics, with the impact felt initially in warehouse operations and then moving into delivery of goods/materials. If Amazon is already seriously contemplating delivery-by-drone, I cannot believe they are not also planning on automating warehouse operations to a greater extent than they already have.”
Mike Osswald, vice president for experience innovation at Hanson Inc., wrote, “Many jobs—truck drivers, customer support, light assembly, bank tellers and store checkout staff—will be diminished for businesses who can afford the upfront implementation costs. People will be displaced, businesses will slowly transition their older workforce to different jobs and not hire younger people or veterans. Businesses who let go of many people when adding robots will face backlash from citizens, but only for a time.”
Tom Folkes, an Internet professional, replied, “We will shortly be able to replace low level information workers—these being teachers, lawyers and librarians. In the not distant future, taxi, bus, and truck drivers. Delivery and food workers will be replaced by 3D printing. The number of people required to develop these systems will be relatively small.”
As the split between highly skilled workers and others continues to grow, current problems with inequality are going to get even worse
A number of these experts offered thoughts on how advances in AI and robotics may lead to increased income inequality and contribute to the ongoing hollowing-out of the middle class.
Bob Briscoe, chief researcher in networking and infrastructure for British Telecom, replied, “Robotics is more likely to have displaced blue-collar jobs, deepening the divide between the haves and the have-nots, and protecting the ‘haves’ from withdrawal of labor and similar industrial action. Rather than increasing leisure time, the ‘haves’ will use the freed-up time to achieve more, because maintaining the previous level of achievement would be rewarded less (relative to a living wage). The greater intensity of economic activity will maintain employment for blue-collar workers, but with similar levels of unemployment as today.”
Robert Cannon, Internet law and policy expert, wrote, “During the Industrial Revolution, although Adam Smith will disagree, our economy has been based primarily on labor. The Industrial Revolution displaced labor from agriculture to the city—but the labor existed. Where there was work to be done, humans were the best “machines” to do the labor. The humans would be paid for their labor; the humans would then pay for goods produced by other people’s labor. As production became more efficient, labor continued but moved into non-essential vocations (where essential is food and shelter). In the future, that foundation of our economy—labor—will be gone. Humans will not be the best “machines” to get work done. What will be left? Capital (ownership) and creativity (human contribution), and perhaps competition (sports, other competitions of humans as we are keen on the realization of the best among us). This will be a massive displacement of the middle class. There will be an ownership class and there will be a poor class that works at a rate below what would economically justify bringing in automation.”
S. Craig Watkins, a professor and author based at the University of Texas-Austin, replied, “This is already happening and while the rise of intelligent machines will contribute to the loss of jobs it will also create new jobs—managing, designing, building, and managing the new systems that will emerge. The challenge is will those new jobs require high skills that only a select portion of the population will be able to acquire? In general, the jobs loss will not likely be matched by the jobs created, thus creating a net loss of jobs overall.”
Henning Schulzrinne, an Internet Hall of Famer and technology developer and professor at Columbia University observed, “Many routine information aggregation and information routing jobs (e.g., in sales, customer support, health care and legal support) will be endangered, as well as some janitorial tasks. I don’t see self-driving cars displacing livery or truck drivers, as they are more likely to be used for parts of driving (e.g., on interstates) or to support drivers. You still need to unload delivery trucks, for example. However, in some cases, jobs won’t be replaced, but rather be down-skilled or bifurcated into a small number of high-skill, high-pay and a much larger number of low-skill, low-pay positions.”
John Anderson, director of broadcast journalism at Brooklyn College, wrote, “It’s the same pattern we saw in manufacturing: the de-skilling of some forms of work due to improvements in technology. The social consequences are also the same: displacement, increased insecurity, growing inequality.”
A professor of communication at the University of Southern California and well-known researcher of Internet uses and users replied, “I worry that these technological developments will further erode opportunities for working class labor in the United States and around the world, further destabilizing the employment situation for many people and further exaggerating the divide between have and have not. I don’t think smashing the machines has ever worked as a response to such developments, but this points all the more urgently to the needs of governments and citizens to more directly address inequalities in economic opportunity.”
A private law firm partner specializing in telecommunications and Internet regulatory issues wrote, “The ability of robots and AI to take on many basic tasks and jobs will relentlessly increase. That means that our total output/production may well increase even as the number of people required to generate that production goes down. That will create vexing problems of distribution of wealth/income, as the folks who own the robots etc. will claim entitlement to all or nearly all the production—yet the ability of people to buy that production will be in the aggregate declining. Over time (again, decades, not 11 years) I suspect that there will be a move towards, and an increase in the value of, unique personal-service type jobs. But that will simply highlight the conflict between different groups.”
A college professor wrote, “This has already begun happening. If we’re lucky, we’ll all be put on middle-class welfare to keep people from becoming destitute and desperate. We are not creative enough to make meaningful jobs out of nothing—and that’s what we’ll be left with when we give all the skilled labor and unskilled labor to the machines.”
An Internet engineer and machine intelligence researcher responded, “With the erosion of manufacturing and manual labor jobs, the underpinning economies of the lower and middle classes have been and will continue to be undermined. Wealth will continue to migrate towards the select few who have control over information resources. The control of information will be markedly enhanced by advances in machine intelligence.”
Mikey O’Connor, one of two elected representatives to ICANN’s GNSO Council, representing the ISP and connectivity provider constituency, wrote, “There will always be a LOT of jobs that are more cheaply performed by extremely low-wage humans than technology. Life at the grinding bottom of the income ladder will be largely unchanged, with any hope of improvement coming from other sectors and technologies. Life in the middle will be changed dramatically. A decreasing few will graduate into wealth and comfort, while most will slip towards the bottom. The middle will continue to become a smaller proportion of the population. Robotic and AI technology, once hoped to mitigate this trend, again disappoints. Professionals are coming under increasing pressure and have joined the middle class on the knife-edge between jumping up or sliding down. Their lives will become ever more stressful as they fight to maintain their position. Life at the top will not change much, although it will be more luxurious (if that’s possible to imagine).”
Oscar Gandy, an emeritus professor at the Annenberg School, University of Pennsylvania, wrote, “If ‘displaced’ means or includes ‘replaced with lower paying jobs’, there is no question in my mind about that: this is a process already clearly visible. While not the only determinant, the hollowing out of the middle class that we are seeing is due in no small part to the replacement of mental/creative/analytical workers with software/systems. This can only increase.”
A retired software engineer and IETF participant responded, “To the extent that our culture focuses on monetary value, and to the extent that labor cost has become the primary dimension in which Western corporations are able to optimize, the only way that automation will be permitted to create more jobs than it destroys will be if those new jobs are at substantially lower wages than the existing ones.”
Stuart Umpleby, a systems theory expert and professor at George Washington University, sees these advances leading to a new type of digital divide: “It is very easy to make a digital device that will make a routine decision. This frees up time to do other things. However, it also makes life more complicated, because one then needs to monitor and control one’s digital agents. It also requires a different type of thinking. For example, instead of going to the store to buy food, one needs to learn how to sign on to a website, order food, monitor delivery and payment. One lives increasingly in an informational environment rather than a physical environment. A virtual environment is more easily monitored by businesses and simulated by scam artists. People must learn how to identify scams, which most likely will become more sophisticated. The gap between those who live primarily in a virtual world and those who live primarily in a physical world will grow.”
We run the risk of creating a “permanent underclass”
A notable number of respondents expressed concern that we will see the emergence of a large class of people who have lost their jobs to automation, and who have little hope of gaining the skills needed to obtain meaningful employment in the future.
Bill Woodcock, executive director for the Packet Clearing House, responded, “We’re seeing AI and expert systems beginning to replace or augment customer-service jobs now, and that trend will continue. I believe that’s a good thing, as they’re replacing jobs starting with the most tedious, leaving the ones that require the most critical thinking and ingenuity for humans. As always, people will find ways to occupy themselves, and I believe AI are not a problem here. Far more troublesome is the trend toward greater social divide, that leaves a larger portion of the world’s population in poverty and unable to garner any advantage from self-driving cars or robot vacuum cleaners, because they simply can’t afford cars or vacuum cleaners of any sort, nor services that come with customer service, whether AI or human. Implicit in this question is an assumption about a middle class that still makes up the bulk of the population of Western nations, and to which many developing countries aspire, but which is, in reality, facing a decline if current trends continue.”
A technology writer observed, “Look at yard maintenance, which employs hundreds of thousands. As soon as there’s a safe, cost-effective, lawn-mowing robot, that robot will take over all the lawn mowing jobs there are. Artificial intelligence that will be able to answer questions over the telephone will displace the average call center employee for most calls. Those with only minimal education will be forced even more to the margins of society. Likely there will have to be a new social safety net for those that are simply unable to earn more than a poverty wage.”
Mark Johns, a professor of media studies at a liberal arts college in the U.S., said, “Many manufacturing and service jobs will be eliminated by intelligent agents in the next decade. Social problems associated with a growing “underclass” will increase…The middle class will continue to shrink, and there will be a greater gap between the educated and tech-savvy ‘haves’ and the uneducated ‘have-nots’.”
The research director at a technology trade association responded, “More people will be forced out of growing sectors of the workforce, with downward mobility, unemployment and underemployment resulting. Growing alienation and fear of the future will mark the lives of some members of the baby boomer population. Traditional jobs across the board, from entry-level service jobs through higher-skilled production and intellectually-challenging jobs, will be reduced in number.”
Jamais Cascio, a writer and futurist specializing in possible futures scenario outcomes, sees this new underclass having a gender component when he writes, “Unlike the numerically-controlled factory robots of the 1970s, today’s general purpose machines are designed to be easily-adapted to new job requirements. It won’t just be dropping a robot into the human’s seat…The self-checkout system at many grocery stores is a perfect example of what I mean: we didn’t just build a robot checker, we made machines that split the checkout task with the customer. Digital travel websites replacing travel agents is another example. We’re already seeing some grey-collar and specialized white-collar jobs being absorbed by machines, from legal assistants to surgeons. I expect that to continue, even accelerate. The biggest exception will be jobs that depend upon empathy as a core capacity—schoolteacher, personal service worker, nurse. These jobs are often those traditionally performed by women. One of the bigger social questions of the mid-late 2020s will be the role of men in this world.”
Dan Coates of YPulse responded, “A great thinker in this space is Tyler Cowen who in his book Average is Over outlines a dual track economic reality wherein those who leverage automation enjoy an escalating standard of living, while those displaced by automation descend into a dramatically reduced standard of living.”
A doctoral student in information science at the Universidade Estadual Paulista, in Sao Paolo, Brazil, wrote, “Robots and automatization will only release qualified personnel from heavy duties. But big masses of unqualified people will still be available, but now, competing with machines. Wages will reduce as well as labor protection in advanced countries. In undeveloped countries the situation will remain the similar as today. Big masses of poor people will suffer starvation and pandemias in developing countries.”
Frank Pasquale, a law professor at a state university, wrote, “The key here is not that there is some predetermined path tech will take. Rather, current levels of inequality will be reinforced by robotization as more of these computers are used both to a) do present human-performed jobs better and b) suppress dissent or political action designed to better distribute the gains from technological advance. Think about Occupy Wall Street being dispersed on its first day by land-based robotic policemen and aerial LRADs. They will make the lives of the top 5% or so a virtual paradise, and will surveill and discipline the bottom 95% to keep them in line.”
A principal engineer for Cisco wrote, “Robotics will add a new twist to the global redistribution of manufacturing; if a robot can operate as cheaply in Detroit as in Shenzhen, why pay to ship materials and finished goods around the world? The social consequences will be driven by chronic underemployment and how we choose to manage it economically. Traditional unemployment schemes will not suffice. Some kind of negative income tax based system may be needed to ensure that everyone has enough to live on. Nevertheless a huge social and economic gulf will open up between those who work (even occasionally), and those who never work, and this will have dramatic political consequences.”
If we aren’t careful, increased income inequality and mass unemployment may lead to social unrest
Taken to their logical extreme, these trends—increased unemployment, widespread inequality, the emergence of a permanent poverty class—caused a number of experts to predict riots and other types of social instability in the relatively near future.
The director of innovation a multi-country company aiming to tap into the gigabit Internet wrote, “I am participating in several international projects to develop agents and to bring about factories of the future (including hybrid factories with a mix of robots and blue-collar workers). I’m also participating in two international projects with major cities as partners, looking at ways of introducing enabling technologies such as the Internet of things. And I live in a city that has been chosen as a test site for replacement of some bus routes by AI-based vehicles. All of this leads me to set the job-displacement date earlier, 2020. And between 2020 and 2025 I expect a lot of social unrest, because insufficient attention is being paid to the needs of people displaced by technology. This will lead to a Winner Take All society, in which such workers can earn 10 or 20 times their current salary. Many of those citizens currently pay for part-time or full-time cleaners, gardeners, handy-people. Most of those local jobs will go (to judge from the people I know who already have robot cleaners and robot mowers). Very, very sad for the people affected.”
A technology risk and cybersecurity expert for a U.S.-based financial services association responded, “We have already observed how automation reduces employment, creates gaps in skills needed to be valued workers in multiple industries including the automotive industry. While it may be more efficient, leads to global trade, and moves complex supply chains, it also creates new challenges and problems for individuals and society. One of these challenges/problems is the gap in the skills and training that is necessary for workers to be valued. Another is increasing income inequality between those that have the valued skills and employed and those who do not and are unemployed or underemployed. Unless industry and government steps in to provide the necessary training, this could lead to greater political unrest.”
A browser engineer at Mozilla wrote, “Current trends indicate that the economy in its current form is ill-suited to support large numbers of low- and un-skilled workers. As more jobs become replaceable, I predict large societal upheavals as the gap between highly skilled (and highly paid) workers and a high proportion of partially, or totally, unemployed people continues to widen.”
| 2014-08-06T00:00:00 |
2014/08/06
|
https://www.pewresearch.org/internet/2014/08/06/views-from-those-who-expect-ai-and-robotics-to-displace-more-jobs-than-they-create-by-2025/
|
[
{
"date": "2014/08/06",
"position": 30,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 30,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
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"query": "robotics job displacement"
},
{
"date": "2014/08/06",
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},
{
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},
{
"date": "2014/08/06",
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"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 34,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 90,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 37,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 36,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
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"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 35,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 32,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
"position": 74,
"query": "robotics job displacement"
}
] |
AI, Robotics, and the Future of Jobs - Pew Research Center
|
AI, Robotics, and the Future of Jobs
|
https://www.pewresearch.org
|
[
"Aaron Smith",
"Janna Anderson",
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"Font-Family Var --Wp--Preset--Font-Family--Sans-Serif",
"Font-Size Font-Weight Gap Important Line-Height",
"Margin-Bottom Text-Transform Uppercase .Wp-Block-Prc-Block-Bylines-Display A Text-Decoration None Important .Wp-Block-Prc-Block-Bylines-Display A Hover Text-Decoration Underline Important .Wp-Block-Prc-Block-Bylines-Display .Prc-Platform-Staff-Bylines__Separator Margin-Left"
] |
The other half of the experts who responded to this survey (52%) expect that technology will not displace more jobs than it creates by 2025. To ...
|
Key Findings
The vast majority of respondents to the 2014 Future of the Internet canvassing anticipate that robotics and artificial intelligence will permeate wide segments of daily life by 2025, with huge implications for a range of industries such as health care, transport and logistics, customer service, and home maintenance. But even as they are largely consistent in their predictions for the evolution of technology itself, they are deeply divided on how advances in AI and robotics will impact the economic and employment picture over the next decade.
We call this a canvassing because it is not a representative, randomized survey. Its findings emerge from an “opt in” invitation to experts who have been identified by researching those who are widely quoted as technology builders and analysts and those who have made insightful predictions to our previous queries about the future of the Internet. (For more details, please see the section “About this Report and Survey.”)
Key themes: reasons to be hopeful Advances in technology may displace certain types of work, but historically they have been a net creator of jobs. We will adapt to these changes by inventing entirely new types of work, and by taking advantage of uniquely human capabilities. Technology will free us from day-to-day drudgery, and allow us to define our relationship with “work” in a more positive and socially beneficial way. Ultimately, we as a society control our own destiny through the choices we make. Key themes: reasons to be concerned Impacts from automation have thus far impacted mostly blue-collar employment; the coming wave of innovation threatens to upend white-collar work as well. Certain highly-skilled workers will succeed wildly in this new environment—but far more may be displaced into lower paying service industry jobs at best, or permanent unemployment at worst. Our educational system is not adequately preparing us for work of the future, and our political and economic institutions are poorly equipped to handle these hard choices.
Some 1,896 experts responded to the following question:
The economic impact of robotic advances and AI—Self-driving cars, intelligent digital agents that can act for you, and robots are advancing rapidly. Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025?
Half of these experts (48%) envision a future in which robots and digital agents have displaced significant numbers of both blue- and white-collar workers—with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order.
The other half of the experts who responded to this survey (52%) expect that technology will not displace more jobs than it creates by 2025. To be sure, this group anticipates that many jobs currently performed by humans will be substantially taken over by robots or digital agents by 2025. But they have faith that human ingenuity will create new jobs, industries, and ways to make a living, just as it has been doing since the dawn of the Industrial Revolution.
These two groups also share certain hopes and concerns about the impact of technology on employment. For instance, many are concerned that our existing social structures—and especially our educational institutions—are not adequately preparing people for the skills that will be needed in the job market of the future. Conversely, others have hope that the coming changes will be an opportunity to reassess our society’s relationship to employment itself—by returning to a focus on small-scale or artisanal modes of production, or by giving people more time to spend on leisure, self-improvement, or time with loved ones.
A number of themes ran through the responses to this question: those that are unique to either group, and those that were mentioned by members of both groups.
The view from those who expect AI and robotics to have a positive or neutral impact on jobs by 2025
JP Rangaswami, chief scientist for Salesforce.com, offered a number of reasons for his belief that automation will not be a net displacer of jobs in the next decade: “The effects will be different in different economies (which themselves may look different from today’s political boundaries). Driven by revolutions in education and in technology, the very nature of work will have changed radically—but only in economies that have chosen to invest in education, technology, and related infrastructure. Some classes of jobs will be handed over to the ‘immigrants’ of AI and Robotics, but more will have been generated in creative and curating activities as demand for their services grows exponentially while barriers to entry continue to fall. For many classes of jobs, robots will continue to be poor labor substitutes.”
Rangaswami’s prediction incorporates a number of arguments made by those in this canvassing who took his side of this question.
Argument #1: Throughout history, technology has been a job creator—not a job destroyer
Jonathan Grudin, principal researcher for Microsoft, concurred: “Technology will continue to disrupt jobs, but more jobs seem likely to be created. When the world population was a few hundred million people there were hundreds of millions of jobs. Although there have always been unemployed people, when we reached a few billion people there were billions of jobs. There is no shortage of things that need to be done and that will not change.”
Argument #2: Advances in technology create new jobs and industries even as they displace some of the older ones
Amy Webb, CEO of strategy firm Webbmedia Group, wrote, “There is a general concern that the robots are taking over. I disagree that our emerging technologies will permanently displace most of the workforce, though I’d argue that jobs will shift into other sectors. Now more than ever, an army of talented coders is needed to help our technology advance. But we will still need folks to do packaging, assembly, sales, and outreach. The collar of the future is a hoodie.”
Argument #3: There are certain jobs that only humans have the capacity to do
A number of respondents argued that many jobs require uniquely human characteristics such as empathy, creativity, judgment, or critical thinking—and that jobs of this nature will never succumb to widespread automation.
Michael Glassman, associate professor at the Ohio State University, wrote, “I think AI will do a few more things, but people are going to be surprised how limited it is. There will be greater differentiation between what AI does and what humans do, but also much more realization that AI will not be able to engage the critical tasks that humans do.”
Argument #4: The technology will not advance enough in the next decade to substantially impact the job market
Another group of experts feels that the impact on employment is likely to be minimal for the simple reason that 10 years is too short a timeframe for automation to move substantially beyond the factory floor. David Clark, a senior research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory, noted, “The larger trend to consider is the penetration of automation into service jobs. This trend will require new skills for the service industry, which may challenge some of the lower-tier workers, but in 12 years I do not think autonomous devices will be truly autonomous. I think they will allow us to deliver a higher level of service with the same level of human involvement.”
Christopher Wilkinson, a retired European Union official, board member for EURid.eu, and Internet Society leader said, “The vast majority of the population will be untouched by these technologies for the foreseeable future. AI and robotics will be a niche, with a few leading applications such as banking, retailing, and transport. The risks of error and the imputation of liability remain major constraints to the application of these technologies to the ordinary landscape.”
Argument #5: Our social, legal, and regulatory structures will minimize the impact on employment
A final group suspects that economic, political, and social concerns will prevent the widespread displacement of jobs. Glenn Edens, a director of research in networking, security, and distributed systems within the Computer Science Laboratory at PARC, a Xerox Company, wrote, “There are significant technical and policy issues yet to resolve, however there is a relentless march on the part of commercial interests (businesses) to increase productivity so if the technical advances are reliable and have a positive ROI then there is a risk that workers will be displaced. Ultimately we need a broad and large base of employed population, otherwise there will be no one to pay for all of this new world.”
Andrew Rens, chief council at the Shuttleworth Foundation, wrote, “A fundamental insight of economics is that an entrepreneur will only supply goods or services if there is a demand, and those who demand the good can pay. Therefore any country that wants a competitive economy will ensure that most of its citizens are employed so that in turn they can pay for goods and services. If a country doesn’t ensure employment driven demand it will become increasingly less competitive.”
The view from those who expect AI and robotics to displace more jobs than they create by 2025
An equally large group of experts takes a diametrically opposed view of technology’s impact on employment. In their reading of history, job displacement as a result of technological advancement is clearly in evidence today, and can only be expected to get worse as automation comes to the white-collar world.
Argument #1: Displacement of workers from automation is already happening—and about to get much worse
[also]
Argument #2: The consequences for income inequality will be profound
For those who expect AI and robotics to significantly displace human employment, these displacements seem certain to lead to an increase in income inequality, a continued hollowing out of the middle class, and even riots, social unrest, and/or the creation of a permanent, unemployable “underclass”.
[said that he]
Alex Howard, a writer and editor based in Washington, D.C., said, “I expect that automation and AI will have had a substantial impact on white-collar jobs, particularly back-office functions in clinics, in law firms, like medical secretaries, transcriptionists, or paralegals. Governments will have to collaborate effectively with technology companies and academic institutions to provide massive retraining efforts over the next decade to prevent massive social disruption from these changes.”
Point of agreement: The educational system is doing a poor job of preparing the next generation of workers
A consistent theme among both groups is that our existing social institutions—especially the educational system—are not up to the challenge of preparing workers for the technology- and robotics-centric nature of employment in the future.
Point of agreement: The concept of “work” may change significantly in the coming decade
On a more hopeful note, a number of experts expressed a belief that the coming changes will allow us to renegotiate the existing social compact around work and employment.
Possibility #1: We will experience less drudgery and more leisure time
Francois-Dominique Armingaud, retired computer software engineer from IBM and now giving security courses to major engineering schools, responded, “The main purpose of progress now is to allow people to spend more life with their loved ones instead of spoiling it with overtime while others are struggling in order to access work.”
Possibility #2: It will free us from the industrial age notion of what a “job” is
A notable number of experts take it for granted that many of tomorrow’s jobs will be held by robots or digital agents—and express hope that this will inspire us as a society to completely redefine our notions of work and employment.
Peter and Trudy Johnson-Lenz, founders of the online community Awakening Technology, based in Portland, Oregon, wrote, “Many things need to be done to care for, teach, feed, and heal others that are difficult to monetize. If technologies replace people in some jobs and roles, what kinds of social support or safety nets will make it possible for them to contribute to the common good through other means? Think outside the job.”
Bob Frankston, an Internet pioneer and technology innovator whose work helped allow people to have control of the networking (internet) within their homes, wrote, “We’ll need to evolve the concept of a job as a means of wealth distribution as we did in response to the invention of the sewing machine displacing seamstressing as welfare.”
Jim Hendler, an architect of the evolution of the World Wide Web and professor of computer science at Rensselaer Polytechnic Institute, wrote, “The notion of work as a necessity for life cannot be sustained if the great bulk of manufacturing and such moves to machines—but humans will adapt by finding new models of payment as they did in the industrial revolution (after much upheaval).”
Tim Bray, an active participant in the IETF and technology industry veteran, wrote, “It seems inevitable to me that the proportion of the population that needs to engage in traditional full-time employment, in order to keep us fed, supplied, healthy, and safe, will decrease. I hope this leads to a humane restructuring of the general social contract around employment.”
Possibility #3: We will see a return to uniquely “human” forms of production
Another group of experts anticipates that pushback against expanding automation will lead to a revolution in small-scale, artisanal, and handmade modes of production.
Kevin Carson, a senior fellow at the Center for a Stateless Society and contributor to the P2P Foundation blog, wrote, “I believe the concept of ‘jobs’ and ‘employment’ will be far less meaningful, because the main direction of technological advance is toward cheap production tools (e.g., desktop information processing tools or open-source CNC garage machine tools) that undermine the material basis of the wage system. The real change will not be the stereotypical model of ‘technological unemployment,’ with robots displacing workers in the factories, but increased employment in small shops, increased project-based work on the construction industry model, and increased provisioning in the informal and household economies and production for gift, sharing, and barter.”
[efforts]
A network scientist for BBN Technologies wrote, “To some degree, this is already happening. In terms of the large-scale, mass-produced economy, the utility of low-skill human workers is rapidly diminishing, as many blue-collar jobs (e.g., in manufacturing) and white-collar jobs (e.g., processing insurance paperwork) can be handled much more cheaply by automated systems. And we can already see some hints of reaction to this trend in the current economy: entrepreneurially-minded unemployed and underemployed people are taking advantages of sites like Etsy and TaskRabbit to market quintessentially human skills. And in response, there is increasing demand for ‘artisanal’ or ‘hand-crafted’ products that were made by a human. In the long run this trend will actually push toward the re-localization and re-humanization of the economy, with the 19th- and 20th-century economies of scale exploited where they make sense (cheap, identical, disposable goods), and human-oriented techniques (both older and newer) increasingly accounting for goods and services that are valuable, customized, or long-lasting.”
Point of agreement: Technology is not destiny … we control the future we will inhabit
In the end, a number of these experts took pains to note that none of these potential outcomes—from the most utopian to most dystopian—are etched in stone. Although technological advancement often seems to take on a mind of its own, humans are in control of the political, social, and economic systems that will ultimately determine whether the coming wave of technological change has a positive or negative impact on jobs and employment.
Seth Finkelstein, a programmer, consultant and EFF Pioneer of the Electronic Frontier Award winner, responded, “The technodeterminist-negative view, that automation means jobs loss, end of story, versus the technodeterminist-positive view, that more and better jobs will result, both seem to me to make the error of confusing potential outcomes with inevitability. Thus, a technological advance by itself can either be positive or negative for jobs, depending on the social structure as a whole….this is not a technological consequence; rather it’s a political choice.”
Jason Pontin, editor in chief and publisher of the MIT Technology Review, responded, “There’s no economic law that says the jobs eliminated by new technologies will inevitably be replaced by new jobs in new markets… All of this is manageable by states and economies: but it will require wrestling with ideologically fraught solutions, such as a guaranteed minimum income, and a broadening of our social sense of what is valuable work.”
| 2014-08-06T00:00:00 |
2014/08/06
|
https://www.pewresearch.org/internet/2014/08/06/future-of-jobs/
|
[
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"date": "2014/08/06",
"position": 48,
"query": "robotics job displacement"
},
{
"date": "2014/08/06",
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"query": "robotics job displacement"
},
{
"date": "2014/08/06",
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"query": "robotics job displacement"
},
{
"date": "2014/08/06",
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"query": "AI unemployment rate"
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"date": "2014/08/06",
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{
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},
{
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{
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{
"date": "2014/08/06",
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"query": "AI unemployment rate"
},
{
"date": "2014/08/06",
"position": 74,
"query": "AI unemployment rate"
},
{
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{
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},
{
"date": "2014/08/06",
"position": 59,
"query": "AI impact jobs"
},
{
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{
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},
{
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] |
Report Finds Rise Of Artificial Intelligence Could Spark ... - IFLScience
|
Report Finds Rise Of Artificial Intelligence Could Spark Mass Unemployment And Inequality
|
https://www.iflscience.com
|
[
"Benjamin Taub",
"Freelance Writer"
] |
Report Finds Rise Of Artificial Intelligence Could Spark Mass Unemployment And Inequality. ... among business owners. This could generate ...
|
Fears about human workers losing their jobs to machines have been fueled by a 72 percent increase in the number of industrial robots in the U.S. over the past decade, although with investment in artificial intelligence (AI) soaring, things could be about to get a lot worse. This is according to a new 300-page report by Bank of America Merrill Lynch (BAML), which details the potential impact of the impending robot revolution on the job market.
The report claims that advances in robotics and AI are leading to a phenomenon known as “creative disruption,” whereby benefits in the shape of increased productivity and reduced costs are offset against disruptions to labor markets, with huge numbers of workers set to lose out. For instance, a San Francisco-based start-up has created a fully-automated burger-flipping machine, which is being tipped to replace workers in fast food restaurants. Elsewhere, plans have been announced to introduce “fully intelligent robot” police officers in the United Arab Emirates before the end of the decade, with the intention of providing “better services without hiring more people.”
While this may sound more like science fiction than reality, the report insists that such innovations are not beyond the realms of possibility, largely thanks to a predicted three-fold increase in the size of the robotics market over the next five years. As a result, it is claimed that “the combination of AI, machine learning, deep learning, and natural user interfaces (such as voice recognition) are making it possible to automate many knowledge worker tasks that were long regarded as impossible or impractical for machines to perform.”
According to Ray Kurzweil, director of engineering at Google, this could soon lead to what he calls the “Singularity,” whereby sentient devices overtake humans as the most intelligent beings on the planet.
On a slightly less apocalyptic but equally alarming note, the BAML report suggests that the rise of automated workforces could result in social and economic inequality, as wealth becomes concentrated among business owners. This could generate “winner-takes-all and monopolistic outcomes,” with the proprietors of technological patents accumulating huge amounts of wealth while unskilled workers struggle.
However, BAML also recognizes that similar fears have been raised several times in the past, with the catastrophic consequences of increased mechanization ultimately failing to materialize. For instance, while the introduction of technology to agricultural processes may have replaced man hours on farms, it also led to the creation of entirely new job markets and has not therefore led to mass unemployment.
Many will be hoping for a similar effect as new robotic technologies continue to revolutionize the global workspace, leading to what BAML is calling a “paradigm shift which will change the way we live and work.”
| 2015-11-09T00:00:00 |
2015/11/09
|
https://www.iflscience.com/artificial-intelligence-could-cause-mass-unemployment-and-inequality-31868
|
[
{
"date": "2015/11/09",
"position": 96,
"query": "AI unemployment rate"
},
{
"date": "2015/11/09",
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"query": "AI unemployment rate"
},
{
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},
{
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},
{
"date": "2015/11/09",
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},
{
"date": "2015/11/09",
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"query": "AI unemployment rate"
},
{
"date": "2015/11/09",
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"query": "AI unemployment rate"
},
{
"date": "2015/11/09",
"position": 51,
"query": "AI unemployment rate"
},
{
"date": "2015/11/09",
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},
{
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{
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{
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] |
The Robots Are Coming … to Take Your Job - Knowledge at Wharton
|
The Robots Are Coming … to Take Your Job
|
https://knowledge.wharton.upenn.edu
|
[] |
Today, robots are increasingly handling many jobs in manufacturing that were done by human hands not more than 20 years ago.
|
Today, robots are increasingly handling many jobs in manufacturing that were done by human hands not more than 20 years ago. This sea change has affected a variety of industries, and it’s one reason why the jobs recovery of the past few years hasn’t included as many manufacturing jobs. Those jobs weren’t just destroyed — they were lost to smart machines.
But while we’re living in a time when computer programs already dominate Wall Street, and when driverless cars and delivery drones are moving from science fiction to mundane fact, those developments may be just the tip of the iceberg. Martin Ford, author of Rise of the Robots: Technology and the Threat of a Jobless Future, recently appeared on the Knowledge at Wharton show on Wharton Business Radio on SiriusXM channel 111 to talk about how the robot revolution has affected businesses in a host of industries, what it means for jobs in the years ahead, and what other surprises might be on the horizon.
An edited transcript of the conversation appears below.
Knowledge at Wharton: We’ve seen these changes going on. Where does your concern lie for the future?
Martin Ford: It’s really across the board. Traditionally, robots have been in factories, but I think that over the next 10 to 20 years, automation in the form of robots, smart software and machine learning is really going to invade pretty much across the board. It’s going to start impacting jobs at all skill levels. It’s not just going to be about low-wage people who don’t have lots of education. It’s really starting to impact also professional jobs.
Knowledge at Wharton: For years, when you did have robots involved, they were viewed as a supplement to the workers, but that clearly isn’t the case now in some areas. From what you’re saying, we’re going to see this even more going forward.
Ford: That’s right. I really think we’re in the midst of a transition where in the past, machines have always been tools that have been used by people and made those people more productive, but increasingly, the technology is really becoming a replacement or a substitute for more and more workers. That’s going to be a huge issue over the coming decade.
Knowledge at Wharton: Based on the level of adoption by businesses so far, what has been the effect on the economy? It’s probably somewhat marginal at this point, but it’s certainly going to be growing.
Ford: That’s right. Clearly, we have not seen actual massive unemployment as a result of this. That’s obvious. But what we have seen is, for decades now, wages have been stagnant, and even now, as the economy has been recovering and we’ve seen the unemployment rate falling, we haven’t seen anything in terms of wage increases. I do think that technology is probably one of the main forces that’s driving that stagnation of wages. It’s important to note the way that stagnation is happening — even as, over the long run, productivity has continued to increase. We see this decoupling of productivity and wages that really points to this transition that’s unfolding.
Knowledge at Wharton: You work in software development, so this is an area that you have focused on professionally for quite some time. How quickly are we going to see this continue to grow? I was watching a segment on a TV show earlier today where they were showing off a driverless pick-up truck. That technology is getting closer and closer to being a part of our everyday life.
Ford: That’s right. All of this is subject to a continuing acceleration, and for that reason, it’s going to unfold at a rate that may surprise us. To take the example of driverless cars: It’s just a few years ago — really, back in 2009 — that I wrote my first book on this topic, and I never imagined at that time that driverless cars would be feasible any time soon. It seemed like an almost impossible task, even to me. Yet now, virtually every auto manufacturer, as well as a whole bunch of companies that haven’t traditionally been in the car industry, are working on this, and it’s looking like it’s going to be feasible within 10, 15 years, at least. So it’s pretty amazing how fast things are moving.
Knowledge at Wharton: You expect that in a lot of the normal, white-collar jobs, we’re going to see more and more robot adoption and automation come into play. We certainly see it in some respects already.
“It’s not about the skill level or how much education you have. Really, the primary question is, is the job on some level routine, repetitive and predictable?”
Ford: On Wall Street, most trading is now done by algorithms. There have been lots and lots of jobs that have disappeared already, and again, the important thing is that in many cases, these are skilled jobs. It’s not about the skill level or how much education you have. The primary question is, is the job on some level routine, repetitive and predictable? In other words, can the actions that a worker undertakes in that field be predicted based on what they’ve done in the past?
If the answer to that is yes, then it’s going to be susceptible to machine learning, which is really the central technology that’s driving all of this. It’s a huge range of jobs, and it includes a lot of jobs that are good jobs that people need to go to school for. So that really kind of throws a wrench into our conventional thinking about how all of this has worked in the past.
Knowledge at Wharton: What are some of the areas that appear to be on the cusp of seeing this great change that you’re talking about and will see greater adoption of robots?
Ford: We already see systems that are beginning to impact journalism that can crank out news stories based on data streams. We see the field of law being impacted, with algorithms that do document review taking over a lot of the more routine work that used to be done by lawyers and paralegals. A lot of that is driven by machine learning, and is going to scale across a whole bunch of the knowledge economy. I can imagine that over the next couple of decades anyone who has a job sitting in front of a computer doing something that is some on level routine and predictable — cranking out the same analysis or the same report every month — that type of thing is going to be susceptible to this. That’s an enormous number of white-collar jobs out there. At the same time, there’s going to be a huge impact on many more routine, lower-skill jobs as well — areas like fast food, driving vehicles. So it’s really very, very broad-based.
Knowledge at Wharton: It is interesting you mention even fast food. We’ve already started to see places like McDonald’s in some locations put in automation where you don’t have the connection with a person up at the counter to put in your order. You’re just going to put your order in at a menu board, and eventually that food’s going to be prepared by somebody, but it’s going to come out to you without you talking to anybody.
Ford: That’s right. There are companies also working on the preparation, what’s happening in the back. There’s a company in San Francisco that’s working on a hamburger robot that can crank out about 400 hamburgers an hour. So you’re eventually going to see automation both in the front, at the counter in terms of the ordering, and also in the back in terms of the food preparation. That again scales to any kind of fast food or beverage — Starbucks, everything. So it’s inevitable that there will be an impact there, and those are jobs that a lot of people rely on. In many cases, people take jobs in fast food because they don’t have better opportunities. Those are a last resort, almost a safety net for workers who don’t have opportunities.
Knowledge at Wharton: You mentioned education a little bit ago, obviously, from the aspect of how it plays in to the whole process because of algorithms being such an important piece. But what about the educational system as a business entity? How will it be changed going forward?
“There’s going to be a huge impact on many more routine, lower-skill jobs as well — areas like fast food, driving vehicles. So it’s really very, very broad-based.”
Ford: We see already the beginnings of that. There’s a lot of focus right now on so-called MOOCs or massive open online courses. There are essentially robotic teaching systems that are becoming more and more powerful, where, for example, a student can use the system online, and there will be a tutor who will monitor their progress and help assign them tasks and adjust the level of difficulty and so forth based on their capabilities. So you’re seeing some pretty important advances there. Right now, education is one of the sectors that has been kind of lagging. If you look at manufacturing vs. education — there have been just tremendous increases in manufacturing. In education, we haven’t seen that, but there are reasons to believe that we may be on the brink of a big disruption. But it’s important to note that if that happens, it’s going to be kind of a double-edged sword. It will make education a lot more accessible, but at the same time, it could impact a lot of jobs in the education sector.
Knowledge at Wharton: What about the area of artificial intelligence?
Ford: Well, that’s a pretty broad area. What we’re seeing right now are tremendous advances in specialized areas of artificial intelligence: machine learning and a particular area called deep learning, which is based on neural networks. That’s really generating some amazing progress in areas like pattern recognition — systems that can recognize images better than people. Microsoft demonstrated a system that could translate spoken Mandarin into English in real time. Not perfectly, obviously, but the fact that it could do it all was incredible. So we’re really just seeing some amazing advances in specialized areas of artificial intelligence.
Knowledge at Wharton: You also bring up the concept of “the Singularity,” and I wanted you to go into that a little bit. It’s something that I think people that are listening to the show would find very interesting.
Ford: Right. The Singularity is a future time when, essentially, everything is accelerating so rapidly that it becomes almost incomprehensible. The general thinking is that the thing that’s going to bring that about will be true artificial intelligence. It will be when we build a machine that can think and have cognitive ability at the same level as a human being. Eventually, that machine will turn its efforts to building better versions of itself, and those will become a super-intelligence. Then we’ll have this entirely new phenomenon in the universe — something that’s more intelligent than human beings. That’s what’s going to drive this incredible acceleration.
Among the big proponents of the idea of the Singularity are people like Ray Kurzweil who is fairly famous in Silicon Valley. It’s a fascinating concept. There have been some kind of outlandish ideas that, I think, have become attached to it. Many people who believe in the promise of the Singularity also believe that they’re going to live forever because these technologies are going to result in immortality for humans, and that’s fairly controversial.
It’s an interesting concept, and there are some elements of it that are useful, but I also think that there’s a lot of kind of crazy baggage that’s attached to it.
Knowledge at Wharton: And it’s still a fairly long way off.
Ford: Yeah, the most optimistic predictions have it maybe 20 years out, and I would personally guess that if anything like that is going to happen, it’s even further out than that.
“Some of the safest jobs are going to be areas like being an electrician or a plumber or maybe a car mechanic because it’s really hard to build a robot that can do all of those things.”
Knowledge at Wharton: Al is [calling from] Toronto, Canada. Al, welcome to the show.
Al: Thank you.… My question is how do you expect automation to affect trade labor and essentially the most common denominator of construction, which is manual labor?
Ford: Right. First of all, it’s true that many trade jobs are for the moment relatively safe, because a lot of trade jobs require a combination of visual perception, dexterity, mobility as well as problem solving. Some of the safest jobs are going to be areas like being an electrician or a plumber or maybe a car mechanic because it’s really hard to build a robot that can do all of those things. But over the longer term, that would not necessarily be true, and there are also other technologies that could disrupt those jobs in ways that you might not expect. Speaking specifically of construction, building a robot that can do what a construction worker does is really tough — although people are working on it. But one of the things that could really have a big impact is construction-scale 3D printing, where you build these massive 3D printers that can actually lay down a house or a building. There is some experimentation with that already. So automation can take a completely different form than having a robot do what a person now does. And that may actually be the bigger impact over the longer term.
Knowledge at Wharton: There are robots used in the auto industry right now, and obviously, they are not used to the level of being a mechanic. How far of a reach is it to take a robot within the auto industry working on an assembly line, putting the car together, to one that’s able to replace parts in a shop?
“There’s a company in San Francisco that’s working on a hamburger robot that can crank out about 400 hamburgers an hour.”
Ford: That’s going to become increasingly feasible, but it will happen in part because the design of cars will change. In other words, we’ll start to design cars specifically so that they’re very modular and can be perhaps repaired by robots. But if you take your old car from the 1970s, for example, that would be obviously an almost impossible challenge for a robot to repair.
Knowledge at Wharton: How much do you see the health care industry advancing in this area in the next 20, 30 years?
Ford: Health care across the board is one of the areas where we most want a disruption. The cost of health care, especially as our population gets older, is really becoming just a staggering burden — and that’s especially true in the United States. We still have really a problematic health care system relative to other countries in terms of the cost.
I think that it is starting to happen. One of the things you certainly will see is more artificial intelligence leveraged in areas like diagnosis and medicine in general. Some areas of nursing and elder care are beginning to be impacted by robots. That’s being driven, in particular, by Japan, which is doing a lot of research into robotics to help care for the elderly, because they have such a rapidly aging population. So all of that is going to begin to have an impact.
Having said that, some areas of health care, like nursing — really going around and interacting with patients and taking care — is really just tremendously hard to automate. I mean, that requires at this point what we would think of as a science-fiction robot. But we are beginning to see progress.
Knowledge at Wharton: Given that there are natural areas where it’s very tough to replicate the experience of a human being, we can expect these are areas that are going to continue to be strong employment areas going forward. Are we going to have another shift in terms of what jobs people are going to be really pushed toward over the next couple of decades?
Ford: In general, if a college student says to me, “What should I study so that it can be safe?” I think health care is a good bet, if you are doing something that is interacting directly with patients and requires lots of mobility and dexterity … as opposed to, for example, being a radiologist where you’re looking at images and interpreting. That kind of thing can be automated quite easily. But yes, it does foreshadow a shift in terms of where the jobs are going to be. I don’t tend to believe that there will be enough jobs in health care to make up for all of the jobs that are going to be impacted in other areas of the economy. But certainly health care as a particular segment is going to be relatively safe.
| 2016-03-02T00:00:00 |
https://knowledge.wharton.upenn.edu/article/the-robots-are-coming-to-take-your-job/
|
[
{
"date": "2016/03/02",
"position": 80,
"query": "robotics job displacement"
}
] |
|
Robohub roundtable: Job loss through automation, Foxconn ...
|
Robohub roundtable: Job loss through automation, Foxconn controversy
|
https://robohub.org
|
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Automation cutting jobs. Robots stealing jobs. The loss of human workers. How many times have different iterations appeared in the media ...
|
Robohub roundtable: Job loss through automation, Foxconn controversy
Every few weeks, Robohub will post a roundtable chat and discuss an engaging topic relating to robotics. In this edition, we looked at the controversial job loss of 60K jobs by Foxconn. Is this substantial job loss a preview to come with automation, or largely overblown hype? We strongly encourage our Robohub readers to chime in and be part of the conversation!
This chat features Sabine Hauert, Andra Keay, Kassie Perlongo, Yannis Erripis, John Payne, providing a range of perspectives from research, business, and the general public.
Automation cutting jobs. Robots stealing jobs. The loss of human workers. How many times have different iterations appeared in the media with these all too similar headlines? When news broke about Foxconn cutting 60,000 jobs from their factory, mass speculation spread like wildfire. Chinese companies say they are looking to combine people with robotics, and that it will improve product quality whilst also ensure labour supply is not a factor to production. So, is China looking at a long-term gain, with a short-term loss, so they can continue to make their own products on their mainland?
Once the offshore labour capital of the world, China has rising costs and a higher standard of living, making it difficult to find workers. President Xi Jinping called for an “industrial robot revolution” in 2014, looking to place automation’s role in raising productivity. Due to this, Chinese companies are turning to robots, announcing measures — such as subsidies and tax incentives — to encourage ‘more industrial automation and development of home-grown robotics.’ By the end of this year, China will overtake Japan to be the world’s biggest operator of industrial robots, according to the International Federation of Robotics (IFR), an industry lobby group.
But back to Foxconn. After the news broke, they released a statement: “We are applying robotics engineering and other innovative manufacturing technologies to replace repetitive tasks previously done by employees, and through training, also enabling our employees to focus on higher value-added elements in the manufacturing process, such as research and development, process control and quality control. We will continue to harness automation and manpower in our manufacturing operations, and we expect to maintain our significant workforce in China.”
The company still employs more than 1.2 million people. If businesses grow more efficient and competitive due to robotics, are robots in some ways helping to prevent businesses from shutting down, with potentially more people losing jobs? Some tasks currently done by humans can be automated at low-cost while others, such as fine manipulation or visual inspection, will require sophisticated hardware and software to mechanise.
The roundtable began by discussing how robotics alleviates risk with people working in dirty, dangerous conditions. And also how this plays into the government’s plan to infuse capital into the mainland robotics industry over the next few years.
“The increase in automation is partly due to the increase in labour costs, but it is also in response to things that happened, like the factory explosion due to unsafe working conditions, killing people. The government pledged 2 billion yuan in subsidies for companies to install industrial robotics and production lines,” said Andra.
“I remember reading exposés about terrible working conditions. It’s not necessarily a bad thing to be introducing robots,” said Andra. “It may be ‘viewed’ as threatening, but it can be beneficial. So instead of people being pushed into horrible working conditions, robots will take care of it,” said Andra.
((As an aside, Foxconn echoes similar concerns, with Day Chia-Peng, general manager of Foxconn’s automation technology development committee, saying that the company was motivated to focus on this area due to safety concerns and manpower shortages in recent years.))
“There are always going to be people working in some of the most dangerous jobs in society, so there’s a question of whether robotics can improve the quality of some of the worst jobs for these people,” Andra continued.
“People often forget how difficult it is to make a robot. The goal is not to make robots that replace humans, but robots that work alongside humans, performing specific tasks. There is a large push to develop collaborative robots (cobots) that are easy to program and deploy by workers. This will allow the workers to focus on more fulfilling high-level tasks that require human-level cognition, and the robots, to focus on specific repetitive tasks,” said Sabine.
The roundtable also discussed China’s strategy for investing and utilising industrial robotics. As noted by the Financial Times, their working age population is expected to fall from one billion people last year to 960 million in 2030, and 800 million by 2050. Automation can be a way to fill the labour gap.
“China has increased their robotics to 1 million robots in 2 years,” said Andra. “The number of robots of China is increasing. They have a middle class that is ageing, and they are expecting a higher standard of living and a higher quality of job. The cost of labour in China has increased, and manufacturing has shifted to other parts of the world.”
“What we’re seeing today, is that robots introduced in factories have contributed overall to an increase in productivity. Higher production leads to more jobs down the road in other sectors such as sales, customer support, and for the overall deployment, installation, maintenance, and management of high-tech factories. While it’s true that the nature of work might change, the net number of jobs may not necessary decrease. The question is how to retrain workers,” said Sabine.
The roundtable mentions that China has relied on a seemingly endless supply of cheap labour for decades to power its economic expansion. “Fast forward to today and the production of robots and China is now the largest consumer of industrial robots and is approaching becoming one of the largest producers,” said Andra.
“Is there also a problem that it’s all happening too quickly and not a gradual pace for people to find new jobs?” asked Kassie.
“I think the pace the automation is increasing, but we’re still talking about a fairly small density,” said Andra.
“One hope is that the jobs of tomorrow are more fulfilling. The nature of work has always evolved, driven in part by the introduction of technology. We don’t manually wash our clothes anymore, and I don’t see why we should be performing the same repetitive task on an assembly line either,” said Sabine.
“Job displacement doesn’t mean everyone needs to become an engineer, although we are very much in need for more people, especially women, in the STEM fields. There is also room for jobs that profoundly build on human interactions and creativity. In fact, I hope all professions will become more human,” said Sabine.
“Augmented reality will make training easier and human workers more effective,” said John. “By replacing shortages of health care professionals (not doctors) robots can help here — replacing tasks, not replacing jobs. I don’t think we have to leave people out of the meaningful economy. Eyes, brains, and hands are wonderful things. Add augmented reality and you have a powerful combination.”
Final thoughts…
In 2013, the most widely noted report on the subject came from Oxford University’s Carl Benedikt Frey and Michael Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?” However, a new report has also been making waves. The report from Organisation for Economic Co-operation and Development (OECD), “The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis,” by Melanie Arntz, Terry Gregory, and Ulrich Zierahn, finds that there actually will be jobs for people, but still foresees difficulties, particularly for low-skilled workers. It’s also important to note that these reports do not specifically discuss robotics, but also software automation.
Also, just because something can be automated doesn’t mean it necessarily will be. Although we have current technology to purchase coffee by machine, there are still people lining up at coffee houses, like Starbucks, to order their favourite brew. The human aspect is more important.
Finally, the robotic component of this overhaul in China will be about more than just installing more robots in manufacturing plants; and perhaps, robots shouldn’t be seen as a cure-all solution for the nation’s labour shortages and economic slowdown.
Read some of our previous Robohub roundtables below:
See all the latest robotics news on Robohub, or sign up for our weekly newsletter.
Robohub Editors
tags: c-Industrial-Automation
| 2016-06-21T00:00:00 |
https://robohub.org/robohub-roundtable-job-loss-through-automation-foxconn-controversy/
|
[
{
"date": "2016/06/21",
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"query": "robotics job displacement"
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"date": "2016/06/21",
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"date": "2016/06/21",
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"date": "2016/06/21",
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"date": "2016/06/21",
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"date": "2016/06/21",
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"date": "2016/06/21",
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"query": "robotics job displacement"
},
{
"date": "2016/06/21",
"position": 72,
"query": "robotics job displacement"
}
] |
|
A Good Disruption - SYSTEMIQ
|
A Good Disruption
|
https://www.systemiq.earth
|
[] |
Artificial Intelligence (AI) can play a powerful role in supporting climate action while boosting sustainable and inclusive economic growth.
|
Disruptive technology is one of the defining economic trends of our age with companies such as Airbnb, Uber and Apple dramatically transforming their industries. But what is the true impact of such disruption on the world’s economies, and does it have the potential to solve global problems such as low growth, inequality and environmental degradation? Why not seize this opportunity and make it a good disruption?
A Good Disruption highlights some of the huge costs that are at stake and argues that managing such disruption will be the defining business challenge of the next decade. In order for us to meet that challenge, the book sets out a bold, timely and inspirational vision for a more robust and sustainable economic model, demonstrating how we can use technology to have a prospering economy while nature thrives.
Marrying the latest thinking from Silicon Valley, the Paris climate meetings and traditional economics, A Good Disruption is rich in relevant case studies, incorporates industry examples from around the world and features a number of interviews with leading business influencers including Nobel laureate Mohamed Yunus and Lord Nicholas Stern.
from left to right: Per-Anders Enkvist, Dr. Klaus Zumwinkel, Dr. Martin Stuchtey
| 2016-10-20T00:00:00 |
https://www.systemiq.earth/resource-category/a-good-disruption/
|
[
{
"date": "2016/10/20",
"position": 95,
"query": "AI economic disruption"
},
{
"date": "2016/10/20",
"position": 94,
"query": "AI economic disruption"
}
] |
|
Blame Displacement of Jobs on Automation, Not Offshoring and ...
|
Blame Displacement of Jobs on Automation, Not Offshoring and Immigration
|
https://futurism.com
|
[] |
High-profile personalities such as Stephen Hawking, as well as economists, have begun to shine the spotlight on this issue of technological ...
|
A New Wave of Automation
As the world continues to achieve unprecedented levels of advancement in AI and robotics, we must, at the same time, come to terms with the fact that our fundamental understanding of technology is also being challenged. Technology was once viewed as a tool that drove human progress forward. Today, technology is threatening the employment and job security of millions.
High-profile personalities such as Stephen Hawking, as well as economists, have begun to shine the spotlight on this issue of technological unemployment—the displacement of human jobs by increasingly sophisticated means of automation.
Eatsa, an automated restaurant chain where customers have zero interaction with a human staff. Credit: Jason Henry for The New York Times.
In a column published in The Guardian, Hawking points out that, “[...]the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining.”
Economists are not discounting the fact that globalization is at least partially to blame for unemployment. They cite trade relations with China during the 2000s as an example, which according to researchers from MIT, led to the loss of over two millions jobs. Still, the impact of automation will have a greater, more disruptive effect on the labor force.
The New Economy
Some argue that the situation isn’t nearly as dire as some imagine it to be.
Elon Musk, who believes that rising automation will lead to the implementation of universal basic income, sees it as an opportunity. "People will have time to do other things, more complex things, more interesting things," says Musk. "Certainly more leisure time."
It’s also entirely possible that as industries begin to assimilate technology into their business models, that it will create new jobs.
“It’s literally the story of the economic development of the world over the last 200 years...just as most of us today have jobs that weren’t even invented 100 years ago, the same will be true 100 years from now,” argued Marc Andreesen, a venture capitalist who was also responsible for creating Mosaic, the first widely used web browser.
Automation can also serve to complement human skills. As Stefan Hajkowicz illustrated in his article in The Conversation: “Spreadsheets didn't kill off accounting jobs. On the contrary, smart accountants learned how to use spreadsheets to become more productive and more employable.”
True. But experts think this industrial revolution is different.
Machines right now might be only capable of doing repetitive, formulaic jobs, but even so, it was already enough to displace thousands of human workers. What happens when prototypes of robots that were taught to mimic the human mind become available? It’s not hard to imagine that knowledge-based, creative, and service-oriented jobs will eventually be overtaken as well.
Our society is evolving—this is the inescapable reality, and change is the watchword of our age. Uncertainty and fear are the inevitable corollaries of the enormous changes stealing upon us; we feel as the cotton picker must have felt at the arrival of the cotton gin, or the coachman beholding the first horseless carriage. Some speak of a melding of our biological minds with the mechanical AI we create, and new phases of human evolution; but these are remote fantasies, of small comfort to the man or woman whose livelihood is rendered obsolete by the march of progress.
But our species' most remarkable trait is its adaptability—with any luck, we'll weather this storm as we've weathered so many before, and doubtless the people of 2117 will marvel at and even long for our quaint, unsophisticated age and our uncomplicated lives.
| 2017-01-21T00:00:00 |
https://futurism.com/2-evergreen-blame-displacement-of-jobs-on-automation-not-offshoring-and-immigration
|
[
{
"date": "2017/01/21",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 97,
"query": "robotics job displacement"
},
{
"date": "2017/01/21",
"position": 99,
"query": "automation job displacement"
},
{
"date": "2017/01/21",
"position": 94,
"query": "robotics job displacement"
}
] |
|
Robot taxes and universal basic income: How do we manage our ...
|
Robot taxes and universal basic income: How do we manage our automated future?
|
https://newatlas.com
|
[
"Stefan Bohrer",
"Rich Has Written For A Number Of Online",
"Print Publications Over The Last Decade While Also Acting As Film Critic For Several Radio Broadcasters",
"Podcasts. His Interests Focus On Psychedelic Science",
"New Media",
"Science Oddities. Rich Completed A Masters Degree In The Arts Back In Before Joining New Atlas In",
"Bob Stuart"
] |
Elon Musk maintains that the idea of a universal basic income is the best solution, while Bill Gates advocates for a robot tax. It's undeniable, ...
|
As more and more jobs are becoming automated, the world faces a dramatic shift in the underlying structures of its labor economies over the next 20 to 50 years. The conversation is slowly becoming more prominent in the mainstream with several major figures highlighting the problem and proposing different solutions. Elon Musk maintains that the idea of a universal basic income is the best solution, while Bill Gates advocates for a robot tax.
It's undeniable, we are entering a revolution in our labor economy. Numerous recent reports have reached some confronting conclusions as to the effects of automation and artificial intelligence on our current work force. A striking report from Oxford University in 2013 estimated that about 47 percent of the total current US work force is at risk of becoming redundant due to automation or artificial intelligence. Another study in 2015 found that 45 percent of jobs in the US right now could be replaced by currently demonstrated technologies.
Late in 2016, Obama's White House released a report warning that measures needed to be taken to manage the millions of jobs that could be lost in the coming years due to technological advances. Despite societies having faced similar labor transitions in the past due to technological advancements displacing workers, we seem to be speeding through a transitional phase at a pace that may exceed our ability to naturally adapt. In an interview with Wired in 2016, President Obama expressed his concern saying, "I do think that we may be in a slightly different period now, simply because of the pervasive applicability of AI and other technologies."
The two big solutions to our looming unemployment crisis currently hitting the mainstream conversation are the institution of a tax on robots and a universal basic income.
How do we tax robots?
Billionaire philanthropist Bill Gates came out in favor of a robot tax in a recent interview with Quartz. Gates explained that in our current climate you have workers who earn income that is taxed, but when their job is replaced by a robot you are losing that income tax revenue. The clearest solution he sees is to tax the robots at a similar level to the human worker it has displaced.
Bill Gates thinks we should tax the robot that takes your job
The revenue garnered from these robot taxes could then be used to support and retrain those unemployed workers, ultimately moving them into new forms of employment. It's an enticingly simple solution to a complex global problem. It's also becoming popular in certain political arenas, with prominent advocacy from surprise socialist front-runner in the upcoming French election, Benoit Hamon.
So what's wrong with a robot tax?
Well, apart from a confusing burden of implementation (for example, how much automation in a job would equal a taxable rate?), this really amounts to a tax on businesses that would inevitably result in a trickle down to the consumer. If a business was to make the decision to automate a percentage of its workforce, the technology itself would be a significant up-front cost before even adding an ongoing robot tax. The tax would ultimately either slow the rate of automated adoption and robotic development, or result in a sharp inflation of costs to the general public.
This is, of course, a simplistic interpretation of events, but not an unreasonable one. In fact, just recently the European Parliament debated this very issue, and while approving a raft of robot law proposals to regulate and manage the growing industry, they roundly rejected the idea of a robot tax.
The big alternative being touted around the world by many is the idea of a universal basic income.
Elon Musk is an outspoken advocate of universal basic income Steve Jurvetson (CC BY 2.0)
Money for nothing
Elon Musk has been a vocal proponent of a universal basic income (UBI) for several years now. Most recently in February 2017 he reiterated his support of the idea at a summit in Dubai saying, "I don't think we're going to have a choice, I think it's going to be necessary. There will be fewer and fewer jobs that a robot cannot do better."
UBI is an even more simplistic solution to our oncoming problems than the robot tax. The idea proposes that all citizens of a country receive a regular unconditional sum of money, either monthly or annually, that is calculated to cover basic living expenses. The UBI is touted as being an efficient way to replace the unwieldy bureaucratic mechanisms of many governmental social welfare systems.
Economists are still debating the cost-effectiveness of UBI, but there are many who believe that the total cost of current large and inefficient welfare systems are higher than the potential cost of UBI. Charles Murray, a prominent advocate of UBI has prolifically written of a proposal to eliminate all current welfare systems in the United States and replace them with a universal US$10,000 guaranteed income for every citizen. He estimated that this would be cheaper than the combined costs of current systems in place, but that has been debated with others claiming his numbers are fundamentally wrong.
When his proposal was put to a panel of expert economists, 58 percent either disagreed or strongly disagreed that it was a better policy than the status quo. It is worth noting many economists surveyed in that particular poll explained that they were responding more to the generalized extremity and lack of nuance in Murray's specific system.
Pushing aside the economic factor for a moment, the most heated debates surrounding an implementation of UBI tend to be concern over the social consequences. After all, if people didn't need to work then why would they? Would we basically be funding a future of lazy, unmotivated human beings?
A group of activists in Switzerland have started up the collective 'Robots For Basic Income' Gen. Grundeinkommen/CC 2.0
But what would we all do?
This is a common concern railed against UBI, but it may be more of a philosophical concern than a pragmatic one. Several pilot UBI projects have shown that households receiving cash handouts have actually increased their labor and production outputs. In India several NGOs piloted UBI and found that those who received the grants doubled their production work when compared to similar households not receiving the grants.
An extensive trial in a small Canadian town in the 1970s produced similar results, showing the guaranteed income system resulted in a larger volume of high school students reaching the 12th grade, as well as an 8.5 percent drop in hospital visits and a reduction in domestic violence cases.
"It's surprising to find that it actually works, that people don't quit their jobs," remarked Evelyn Forget, a sociology professor who recently reevaluated the records from that Canadian pilot study in the 1970s, "There's this fear that if we have too much freedom, we might misuse it."
In 2013 Swiss activists gathered 125,000 signatures forcing a referendum on the issue of UBI. In 2016 an overwhelming 76 percent of the population voted against implementing the system Stefan Bohrer / WikiCommons
At its core we return to the philosophical questions regarding the general effects of UBI on society. For generations our occupations have guided and structured our lives and our identities. It is undeniable that UBI would alter this, but it could easily be argued that this ideological shift is already happening anyway.
The new generation of millennials are currently redefining older perceptions of work and career. They are known as the job-hopping generation and have be shown to care more about personal fulfilment in their job over money.
These attitudes seem to signal that for the younger generations, UBI would offer a safety net allowing personal creative explorations, enhanced education, and even the ability to take the time to cultivate new income streams from different endeavors. As we look to almost half of our current workforce becoming redundant through automation over the coming years, we will certainly need to be working as a society to develop new industries, occupations and income streams. Is UBI the most straight-forward way to achieve this outcome?
Whatever you believe is the best way forward, be it robot taxes, UBI or its more pragmatic variant, the negative income tax, everyone can agree that our labor economies are facing some dramatic looming changes. Something will certainly need to be done and the discussion needs to be had, so let's have it.
We'd love to hear your thoughts on the topic. Sound off in the comments below.
| 2017-02-21T00:00:00 |
2017/02/21
|
https://newatlas.com/robot-tax-universal-basic-income-future-work/48014/
|
[
{
"date": "2017/02/20",
"position": 94,
"query": "universal basic income AI"
}
] |
Work and social policy in the age of artificial intelligence | Brookings
|
Work and social policy in the age of artificial intelligence
|
https://www.brookings.edu
|
[
"Darrell M. West",
"Mark Maccarthy",
"Eduardo Levy Yeyati"
] |
The White House report, entitled “Artificial Intelligence, Automation and the Economy”, concluded that AI-driven automation suggests the need ...
|
Since its inception some sixty years ago, artificial intelligence (AI) has evolved from an arcane academic field into a powerful driver of social and economic change. AI is now the basis for a wide range of mainstream technologies including web search, medical diagnosis, smart phone applications, and most recently, autonomous vehicles. Deep learning —a form of machine learning based on layered representations or neural networks—has dramatically improved pattern recognition, speech recognition, and natural language processing.
In 2013, the Oxford Martin School published a report predicting that 47 percent of jobs in the United States could be under threat of automation within two decades due to advances in AI technologies. Last year, the Obama administration raised similar concerns in a presidential report on AI. The White House report, entitled “Artificial Intelligence, Automation and the Economy”, concluded that AI-driven automation suggests the need for aggressive public policies and a more robust safety net in order to combat labor disruption. Of course, predictions on total number of job losses is fundamentally uncertain, but according to a recent study by McKinsey, technology is expected to automate portions of jobs over the next decade.
Given the prospects of an economic future in which large swaths of the working population are at risk of losing their jobs or seeing them diminish in quality, how might government mitigate the impact of AI? A recent report from the University of Toronto’s Mowat Centre has explored this conundrum in some detail, outlining potential options for policymakers to consider. In the near-term, and with relatively little difficulty, governments can look to their own mandates and to partnerships with the private sector and organized labor to consider the following social policies:
| 2017-02-28T00:00:00 |
https://www.brookings.edu/articles/work-and-social-policy-in-the-age-of-artificial-intelligence/
|
[
{
"date": "2017/02/28",
"position": 54,
"query": "government AI workforce policy"
}
] |
|
The Jobs That Artificial Intelligence Will Create
|
The Jobs That Artificial Intelligence Will Create
|
https://sloanreview.mit.edu
|
[
"H. James Wilson",
"Paul R. Daugherty",
"Nicola Morini-Bianzino",
"Massachusetts Institute Of Technology",
"About The Authors"
] |
Our research reveals three new categories of AI-driven business and technology jobs. We label them trainers, explainers, and sustainers.
|
The threat that automation will eliminate a broad swath of jobs across the world economy is now well established. As artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur.
It can be a distressing picture.
But here’s what we’ve been overlooking: Many new jobs will also be created — jobs that look nothing like those that exist today.
In Accenture PLC’s global study of more than 1,000 large companies already using or testing AI and machine-learning systems, we identified the emergence of entire categories of new, uniquely human jobs. These roles are not replacing old ones. They are novel, requiring skills and training that have no precedents. (Accenture’s study, “How Companies Are Reimagining Business Processes With IT,” will be published this summer.)
More specifically, our research reveals three new categories of AI-driven business and technology jobs. We label them trainers, explainers, and sustainers. Humans in these roles will complement the tasks performed by cognitive technology, ensuring that the work of machines is both effective and responsible — that it is fair, transparent, and auditable.
Trainers
This first category of new jobs will need human workers to teach AI systems how they should perform — and it is emerging rapidly. At one end of the spectrum, trainers help natural-language processors and language translators make fewer errors. At the other end, they teach AI algorithms how to mimic human behaviors.
Customer service chatbots, for example, need to be trained to detect the complexities and subtleties of human communication. Yahoo Inc. is trying to teach its language processing system that people do not always literally mean what they say. Thus far, Yahoo engineers have developed an algorithm that can detect sarcasm on social media and websites with an accuracy of at least 80%.
Consider, then, the job of “empathy trainer” — individuals who will teach AI systems to show compassion. The New York-based startup Kemoko Inc., d/b/a Koko, which sprung from the MIT Media Lab, has developed a machine-learning system that can help digital assistants such as Apple’s Siri and Amazon’s Alexa address people’s questions with sympathy and depth.
About the Authors H. James Wilson is managing director of IT and business research at Accenture Research. Paul R. Daugherty is Accenture’s chief technology and innovation officer. Nicola Morini-Bianzino is global lead of artificial intelligence at Accenture.
| 2017-03-23T00:00:00 |
2017/03/23
|
https://sloanreview.mit.edu/article/will-ai-create-as-many-jobs-as-it-eliminates/
|
[
{
"date": "2017/03/23",
"position": 58,
"query": "artificial intelligence employment"
},
{
"date": "2017/03/23",
"position": 50,
"query": "AI job creation vs elimination"
},
{
"date": "2017/03/23",
"position": 60,
"query": "artificial intelligence employment"
},
{
"date": "2017/03/23",
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"query": "artificial intelligence employment"
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"date": "2017/03/23",
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"query": "AI job creation vs elimination"
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"date": "2017/03/23",
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"query": "AI job creation vs elimination"
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"date": "2017/03/23",
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"query": "artificial intelligence employment"
},
{
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"query": "artificial intelligence employment"
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},
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{
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"query": "artificial intelligence employment"
},
{
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"query": "AI job creation vs elimination"
},
{
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},
{
"date": "2017/03/23",
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"query": "AI job creation vs elimination"
},
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"query": "AI job creation vs elimination"
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{
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"query": "artificial intelligence employment"
}
] |
Robots, Automation, and Jobs: A Primer for Policymakers | ITIF
|
Robots, Automation, and Jobs: A Primer for Policymakers
|
https://itif.org
|
[
"Robert D. Atkinson"
] |
... robotics and artificial intelligence on employment. This ... Automation does not lead to net job loss, either. Even if automation ...
|
Read foreign language translations: Chinese, French, German, Spanish.
There is considerable interest, if not consternation, about the potential effects of emerging technologies such as robotics and artificial intelligence on employment. There is also considerable confusion about the interaction between automation, technology, and jobs. Here are 13 key points that are important for policymakers to understand about that interaction:
| 2017-05-08T00:00:00 |
2017/05/08
|
https://itif.org/publications/2017/05/08/robots-automation-and-jobs-primer-policymakers/
|
[
{
"date": "2017/05/08",
"position": 98,
"query": "robotics job displacement"
},
{
"date": "2017/05/08",
"position": 98,
"query": "robotics job displacement"
},
{
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"query": "robotics job displacement"
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"query": "robotics job displacement"
},
{
"date": "2017/05/08",
"position": 95,
"query": "robotics job displacement"
}
] |
Is Your Job Safe From the Rise of the Robots? | The Motley Fool
|
Is Your Job Safe From the Rise of the Robots?
|
https://www.fool.com
|
[
"Keith Speights"
] |
The World Economic Forum (WEF) projects a loss of 7.1 million jobs to robots in the world's 15 leading countries, including the US, by 2020.
|
The robots aren't just coming. They're already here. And your job just might be at risk.
Serious threat or overblown hype?
That's the question many have about recent reports that robots could replace millions of jobs currently held by human workers. On one hand, there's the research by global accounting and consulting company PricewaterhouseCoopers (PwC) that projects 38% of American jobs could be at risk of replacement by automation within the next 15 or so years. On the other hand, U.S. Treasury Secretary Steven Mnuchin thinks that we're so far away from seeing artificial intelligence take American jobs that "it's not even on [his] radar screen."
Is your job safe from the rise of the robots? The best answer right now: It depends.
How things stand today
First, when people talk about the rise of the robots, they're not necessarily referring to smart machines that resemble humans. Instead, the general use of the word "robots" includes all technology that can handle tasks that humans have performed in the past. The reality is that robots are already an important part of the U.S. economy.
The top 14 industrial robot manufacturers have an estimated 1.6 million robots in use at manufacturing, distribution, and other sites across the world. The automobile industry uses robots extensively. But while robots have replaced some jobs, U.S. automakers have actually been adding jobs overall during the past several years.
Many robots haven't replaced jobs but have instead augmented them. Intuitive Surgical's (ISRG 0.85%) da Vinci robotic surgical system is a great example.
Surgeons and other medical professionals are still just as involved with procedures as they are without using the robotic system. However, instead of having the surgeon stand over the patient, he or she is several feet away at a console. Da Vinci allows the surgeon to see a crisp, high-definition 3-D image of the patient's anatomy. It translates the surgeon's hand movements on the system's controls into precise micro-movements that enable less-invasive surgery.
Da Vinci was used in over 560,000 surgeries in the U.S. last year. How many jobs were displaced? Zero. The technology actually resulted in a net gain of jobs, since Intuitive Surgical employees nearly 3,800 people.
The near future
The World Economic Forum (WEF) projects a loss of 7.1 million jobs to robots in the world's 15 leading countries, including the U.S., by 2020. Which jobs are most likely to be lost? The WEF paints a dire picture for office and administrative workers, estimating that these occupations will make up roughly two-thirds of the total job losses.
Research firm Forrester (FORR 0.41%) has a similar outlook. The company estimates that cognitive technologies, including robots, artificial intelligence (AI), machine learning, and automation, will replace 16% of U.S. jobs by 2025. Like the WEF, Forrester thinks that office and administrative support positions will be most affected.
It's not all bad news, though. The WEF estimates that 2 million new jobs will be created by 2020, especially in certain skilled positions such as data analysts and specialist sales representatives. Forrester projects that around 8.9 million new jobs will be created in the U.S. by 2025, particularly for robot monitoring professionals, data scientists, automation specialists, and content curators. As a result of this growth, Forrester thinks the overall impact of technology will result in a net loss of 7% of American jobs by 2025.
By the early 2030s
Workers in the U.S. and across the world could face the most significant threat in the years after 2025. And it won't be just office support staff.
PwC thinks job losses in the wholesale, retail, and manufacturing industries could be even worse than in the administrative and support services area. In the U.S., financial and insurance sector jobs could be hit hard.
Adoption of driverless cars could wreak havoc in the transportation industry. Taxi and Uber drivers could be forced to find work elsewhere as AI replaces them. The same could happen with many of the estimated 3.5 million truck drivers in the United States.
In general, workers who spend a significant amount of time performing manual or repetitive tasks will be at most risk of losing their jobs to robots over the next 15 years. PwC projects that the greatest impact will be on employees in jobs that require lower levels of education.
What to do
Some think that teachers, registered nurses, and dental hygienists should be in low danger for a while. Over the longer run, however, there are probably few jobs that can be considered completely safe. The potential for AI is so tremendous that it could be just a matter of time before robots can perform nearly every job that humans have.
What can you do if you have years to go before retirement? Getting more education could help. Perhaps you might want to take a look at some of the new jobs that could be created, such as data scientist.
I like the idea of investing in companies that should benefit from the rise of the robots. Intuitive Surgical would be a good one. So would Google's parent, Alphabet (GOOG 0.85%) (GOOGL 0.79%), which is a pioneer in driverless-car technology and in artificial intelligence, in general. My colleagues at The Motley Fool have identified Alphabet as a top driverless-car stock, as well as a top AI stock to buy. I totally agree with their assessments.
If you can't beat them, join them. Investing now seems like a smart way to join the robots before they take millions of jobs in the future.
| 2017-06-04T00:00:00 |
2017/06/04
|
https://www.fool.com/careers/2017/06/04/is-your-job-safe-from-the-rise-of-the-robots.aspx
|
[
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] |
Artificial intelligence in healthcare: past, present and future
|
Artificial intelligence in healthcare: past, present and future
|
https://svn.bmj.com
|
[
"Author Affiliations"
] |
We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and ...
|
The AI devices: ML and NLP
In this section, we review the AI devices (or techniques) that have been found useful in the medial applications. We categorise them into three groups: the classical machine learning techniques,26 the more recent deep learning techniques27 and the NLP methods.28
Classical ML ML constructs data analytical algorithms to extract features from data. Inputs to ML algorithms include patient ‘traits’ and sometimes medical outcomes of interest. A patient’s traits commonly include baseline data, such as age, gender, disease history and so on, and disease-specific data, such as diagnostic imaging, gene expressions, EP test, physical examination results, clinical symptoms, medication and so on. Besides the traits, patients’ medical outcomes are often collected in clinical research. These include disease indicators, patient’s survival times and quantitative disease levels, for example, tumour sizes. To fix ideas, we denote the jth trait of the ith patient by X ij , and the outcome of interest by Y i . Depending on whether to incorporate the outcomes, ML algorithms can be divided into two major categories: unsupervised learning and supervised learning. Unsupervised learning is well known for feature extraction, while supervised learning is suitable for predictive modelling via building some relationships between the patient traits (as input) and the outcome of interest (as output). More recently, semisupervised learning has been proposed as a hybrid between unsupervised learning and supervised learning, which is suitable for scenarios where the outcome is missing for certain subjects. These three types of learning are illustrated in figure 4. Figure 4 Request permissions Graphical illustration of unsupervised learning, supervised learning and semisupervised learning. Clustering and principal component analysis (PCA) are two major unsupervised learning methods. Clustering groups subjects with similar traits together into clusters, without using the outcome information. Clustering algorithms output the cluster labels for the patients through maximising and minimising the similarity of the patients within and between the clusters. Popular clustering algorithms include k-means clustering, hierarchical clustering and Gaussian mixture clustering. PCA is mainly for dimension reduction, especially when the trait is recorded in a large number of dimensions, such as the number of genes in a genome-wide association study. PCA projects the data onto a few principal component (PC) directions, without losing too much information about the subjects. Sometimes, one can first use PCA to reduce the dimension of the data, and then use clustering to group the subjects. On the other hand, supervised learning considers the subjects’ outcomes together with their traits, and goes through a certain training process to determine the best outputs associated with the inputs that are closest to the outcomes on average. Usually, the output formulations vary with the outcomes of interest. For example, the outcome can be the probability of getting a particular clinical event, the expected value of a disease level or the expected survival time. Clearly, compared with unsupervised learning, supervised learning provides more clinically relevant results; hence AI applications in healthcare most often use supervised learning. (Note that unsupervised learning can be used as part of the preprocessing step to reduce dimensionality or identify subgroups, which in turn makes the follow-up supervised learning step more efficient.) Relevant techniques include linear regression, logistic regression, naïve Bayes, decision tree, nearest neighbour, random forest, discriminant analysis, support vector machine (SVM) and neural network.27 Figure 5 displays the popularity of the various supervised learning techniques in medical applications, which clearly shows that SVM and neural network are the most popular ones. This remains the case when restricting to the three major data types (image, genetic and EP), as shown in figure 6. Figure 5 Request permissions The machine learning algorithms used in the medical literature. The data are generated through searching the machine learning algorithms within healthcare on PubMed. Figure 6 Request permissions The machine learning algorithms used for imaging (upper), genetic (middle) and electrophysiological (bottom) data. The data are generated through searching the machine learning algorithms for each data type on PubMed. Below we will provide more details about the mechanisms of SVM and neural networks, along with application examples in the cancer, neurological and cardiovascular disease areas.
Support vector machine SVM is mainly used for classifying the subjects into two groups, where the outcome Y i is a classifier: Y i = −1 or 1 represents whether the ith patient is in group 1 or 2, respectively. (The method can be extended for scenarios with more than two groups.) The basic assumption is that the subjects can be separated into two groups through a decision boundary defined on the traits X ij , which can be written as: where w j is the weight putting on the jth trait to manifest its relative importance on affecting the outcome among the others. The decision rule then follows that if a i >0, the ith patient is classified to group 1, that is, labelling Y i = −1; if a i <0, the patient is classified to group 2, that is, labelling Y i =1. The class memberships are indeterminate for the points with a i =0. See figure 7 for an illustration with , , a 1 =1, and a 2 =−1. Figure 7 Request permissions An illustration of the support vector machine. The training goal is to find the optimal w j s so that the resulting classifications agree with the outcomes as much as possible, that is, with the smallest misclassification error, the error of classifying a patient into the wrong group. Intuitively, the best weights must allow (1) the sign of a i to be the same as Y i so the classification is correct; and (2) |a i | to be far away from 0 so the ambiguity of the classification is minimised. These can be achieved by selecting w j s that minimise a quadratic loss function.29 Furthermore, assuming that the new patients come from the same population, the resulting w j s can be applied to classify these new patients based on their traits. An important property of SVM is that the determination of the model parameters is a convex optimisation problem so the solution is always global optimum. Furthermore, many existing convex optimisation tools are readily applicable for the SVM implementation. As such, SVM has been extensively used in medical research. For instance, Orrù et al applied SVM to identify imaging biomarkers of neurological and psychiatric disease.30 Sweilam et al reviewed the use of SVM in the diagnosis of cancer.31 Khedher et al used the combination of SVM and other statistical tools to achieve early detection of Alzheimer’s disease.32 Farina et al used SVM to test the power of an offline man/machine interface that controls upper-limb prostheses.22
Neural network One can think about neural network as an extension of linear regression to capture complex non-linear relationships between input variables and an outcome. In neural network, the associations between the outcome and the input variables are depicted through multiple hidden layer combinations of prespecified functionals. The goal is to estimate the weights through input and outcome data so that the average error between the outcome and their predictions is minimised. We describe the method in the following example. Mirtskhulava et al used neural network in stroke diagnosis.33 In their analysis, the input variables X i 1 , . . . , X ip are p=16 stroke-related symptoms, including paraesthesia of the arm or leg, acute confusion, vision, problems with mobility and so on. The outcome Y i is binary: Y i =1/0 indicates the ith patient has/does not have stroke. The output parameter of interest is the probability of stroke, a i , which carries the form of In the above equation, the w 10 and w 20 ≠0 guarantee the above form to be valid even when all X ij , f k are 0; the w 1l and 2l s are the weights to characterise the relative importance of the corresponding multiplicands on affecting the outcome; the f k s and are prespecified functionals to manifest how the weighted combinations influence the disease risk as a whole. A stylised illustration is provided in figure 8. Figure 8 Request permissions An illustration of neural network. The training goal is to find the weights w ij , which minimise the prediction error 2. The minimisation can be performed through standard optimisation algorithms, such as local quadratic approximation or gradient descent optimisation, that are included in both MATLAB and R. If the new data come from the same population, the resulting w ij can be used to predict the outcomes based on their specific traits.29 Similar techniques have been used to diagnose cancer by Khan et al, where the inputs are the PCs estimated from 6567 genes and the outcomes are the tumour categories.34 Dheeba et al used neural network to predict breast cancer, with the inputs being the texture information from mammographic images and the outcomes being tumour indicators.35 Hirschauer et al used a more sophisticated neural network model to diagnose Parkinson’s disease based on the inputs of motor, non-motor symptoms and neuroimages.36
Deep learning: a new era of ML Deep learning is a modern extension of the classical neural network technique. One can view deep learning as a neural network with many layers (as in figure 9). Rapid development of modern computing enables deep learning to build up neural networks with a large number of layers, which is infeasible for classical neural networks. As such, deep learning can explore more complex non-linear patterns in the data. Another reason for the recent popularity of deep learning is due to the increase of the volume and complexity of data.37 Figure 10 shows that the application of deep learning in the field of medical research nearly doubled in 2016. In addition, figure 11 shows that a clear majority of deep learning is used in imaging analysis, which makes sense given that images are naturally complex and high volume. Figure 9 Request permissions An illustration of deep learning with two hidden layers. Figure 10 Request permissions Current trend for deep learning. The data are generated through searching the deep learning in healthcare and disease category on PubMed. Figure 11 Request permissions The data sources for deep learning. The data are generated through searching deep learning in combination with the diagnosis techniques on PubMed. Different from the classical neural network, deep learning uses more hidden layers so that the algorithms can handle complex data with various structures.27 In the medical applications, the commonly used deep learning algorithms include convolution neural network (CNN), recurrent neural network, deep belief network and deep neural network. Figure 12 depicts their trends and relative popularities from 2013 to 2016. One can see that the CNN is the most popular one in 2016. Figure 12 Request permissions The four main deep learning algorithm and their popularities. The data are generated through searching algorithm names in healthcare and disease category on PubMed. The CNN is developed in viewing of the incompetence of the classical ML algorithms when handling high dimensional data, that is, data with a large number of traits. Traditionally, the ML algorithms are designed to analyse data when the number of traits is small. However, the image data are naturally high-dimensional because each image normally contains thousands of pixels as traits. One solution is to perform dimension reduction: first preselect a subset of pixels as features, and then perform the ML algorithms on the resulting lower dimensional features. However, heuristic feature selection procedures may lose information in the images. Unsupervised learning techniques such as PCA or clustering can be used for data-driven dimension reduction. The CNN was first proposed and advocated for the high-dimensional image analysis by Lecun et al.38 The inputs for CNN are the properly normalised pixel values on the images. The CNN then transfers the pixel values in the image through weighting in the convolution layers and sampling in the subsampling layers alternatively. The final output is a recursive function of the weighted input values. The weights are trained to minimise the average error between the outcomes and the predictions. The implementation of CNN has been included in popular software packages such as Caffe from Berkeley AI Research,39 CNTK from Microsoft40 and TensorFlow from Google.41 Recently, the CNN has been successfully implemented in the medical area to assist disease diagnosis. Long et al used it to diagnose congenital cataract disease through learning the ocular images.24 The CNN yields over 90% accuracy on diagnosis and treatment suggestion. Esteva et al performed the CNN to identify skin cancer from clinical images.20 The proportions of correctly predicted malignant lesions (ie, sensitivity) and benign lesions (ie, specificity) are both over 90%, which indicates the superior performance of the CNN. Gulshan et al applied the CNN to detect referable diabetic retinopathy through the retinal fundus photographs.25 The sensitivity and specificity of the algorithm are both over 90%, which demonstrates the effectiveness of using the technique on the diagnosis of diabetes. It is worth mentioning that in all these applications, the performance of the CNN is competitive against experienced physicians in the accuracy for classifying both normal and disease cases.
Natural language processing The image, EP and genetic data are machine-understandable so that the ML algorithms can be directly performed after proper preprocessing or quality control processes. However, large proportions of clinical information are in the form of narrative text, such as physical examination, clinical laboratory reports, operative notes and discharge summaries, which are unstructured and incomprehensible for the computer program. Under this context, NLP targets at extracting useful information from the narrative text to assist clinical decision making.28 An NLP pipeline comprises two main components: (1) text processing and (2) classification. Through text processing, the NLP identifies a series of disease-relevant keywords in the clinical notes based on the historical databases.42 Then a subset of the keywords are selected through examining their effects on the classification of the normal and abnormal cases. The validated keywords then enter and enrich the structured data to support clinical decision making. The NLP pipelines have been developed to assist clinical decision making on alerting treatment arrangements, monitoring adverse effects and so on. For example, Fiszman et al showed that introducing NLP for reading the chest X-ray reports would assist the antibiotic assistant system to alert physicians for the possible need for anti-infective therapy.43 Miller et al used NLP to automatically monitor the laboratory-based adverse effects.44 Furthermore, the NLP pipelines can help with disease diagnosis. For instance, Castro et al identified 14 cerebral aneurysms disease-associated variables through implementing NLP on the clinical notes.45 The resulting variables are successfully used for classifying the normal patients and the patients with cerebral, with 95% and 86% accuracy rates on the training and validation samples, respectively. Afzal et al implemented the NLP to extract the peripheral arterial disease-related keywords from narrative clinical notes. The keywords are then used to classify the normal and the patients with peripheral arterial disease, which achieves over 90% accuracy.42
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| 2017-12-20T00:00:00 |
2017/12/20
|
https://svn.bmj.com/content/2/4/230
|
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Artificial Intelligence Salary | Free-Work
|
Artificial Intelligence Salary
|
https://www.free-work.com
|
[] |
Artificial Intelligence Salary · Research Scientist · *A percentile is a measure used in statistics indicating the value below which a given ...
|
Research Scientist
Job Description --> Research scientists are responsible for designing, undertaking and analysing information from controlled laboratory-based investigations, experiments and trials.
*A percentile is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall. For example, the 20th percentile is the value below which 20% of the observations may be found
Software Engineer
Job Description --> Software engineers tend to specialize in a few areas of development, such as networks, operating systems, databases or applications, and each area requires fluency in its own set of computer languages and development environments.
C# Developer
Job Description --> C# is a modern, general purpose, object-oriented programming language designed around the Common Language Infrastructure. A great C# developer is capable of handling many aspects of developing an application, including but not limited to performance, scalability, security, testing, and more. C# developers can develop modern applications that run on desktop computers, or even sophisticated back-end processes powering modern web applications.
Information Security Engineer
Job Description --> Information Security Engineers, also called Information Security Analysts, help to safeguard organization's computer networks and systems. They plan and carry out security measures to monitor and protect sensitive data and systems from infiltration and cyber-attacks.
Software Development Manager
Job Description --> As a software developer you'll be playing a key role in the design, installation, testing and maintenance of software systems.
Java Developer
Job Description --> A
| 2017-07-18T00:00:00 |
https://www.free-work.com/en-gb/tech-it/blog/it-skills/artificial-intelligence-salary
|
[
{
"date": "2017/07/18",
"position": 84,
"query": "artificial intelligence wages"
}
] |
|
Robots won't steal our jobs if we put workers at center of AI revolution
|
Robots won’t steal our jobs if we put workers at center of AI revolution
|
https://theconversation.com
|
[
"Lee Dyer",
"Thomas Kochan"
] |
One of the McKinsey study's key findings was that about a third of the tasks performed in 60 percent of today's jobs are likely to be eliminated ...
|
The technologies driving artificial intelligence are expanding exponentially, leading many technology experts and futurists to predict machines will soon be doing many of the jobs that humans do today. Some even predict humans could lose control over their future.
While we agree about the seismic changes afoot, we don’t believe this is the right way to think about it. Approaching the challenge this way assumes society has to be passive about how tomorrow’s technologies are designed and implemented. The truth is there is no absolute law that determines the shape and consequences of innovation. We can all influence where it takes us.
Thus, the question society should be asking is: “How can we direct the development of future technologies so that robots complement rather than replace us?”
The Japanese have an apt phrase for this: “giving wisdom to the machines.” And the wisdom comes from workers and an integrated approach to technology design, as our research shows.
Lessons from history
There is no question coming technologies like AI will eliminate some jobs, as did those of the past.
More than half of the American workforce was involved in farming in the 1890s, back when it was a physically demanding, labor-intensive industry. Today, thanks to mechanization and the use of sophisticated data analytics to handle the operation of crops and cattle, fewer than 2 percent are in agriculture, yet their output is significantly higher.
But new technologies will also create new jobs. After steam engines replaced water wheels as the source of power in manufacturing in the 1800s, the sector expanded sevenfold, from 1.2 million jobs in 1830 to 8.3 million by 1910. Similarly, many feared that the ATM’s emergence in the early 1970s would replace bank tellers. Yet even though the machines are now ubiquitous, there are actually more tellers today doing a wider variety of customer service tasks.
So trying to predict whether a new wave of technologies will create more jobs than it will destroy is not worth the effort, and even the experts are split 50-50.
It’s particularly pointless given that perhaps fewer than 5 percent of current occupations are likely to disappear entirely in the next decade, according to a detailed study by McKinsey.
Instead, let’s focus on the changes they’ll make to how people work.
Reuters/John Gress
It’s about tasks, not jobs
To understand why, it’s helpful to think of a job as made up of a collection of tasks that can be carried out in different ways when supported by new technologies.
And in turn, the tasks performed by different workers – colleagues, managers and many others – can also be rearranged in ways that make the best use of technologies to get the work accomplished. Job design specialists call these “work systems.”
One of the McKinsey study’s key findings was that about a third of the tasks performed in 60 percent of today’s jobs are likely to be eliminated or altered significantly by coming technologies. In other words, the vast majority of our jobs will still be there, but what we do on a daily basis will change drastically.
To date, robotics and other digital technologies have had their biggest effects on mostly routine tasks like spell-checking and those that are dangerous, dirty or hard, such as lifting heavy tires onto a wheel on an assembly line. Advances in AI and machine learning will significantly expand the array of tasks and occupations affected.
Reuters/Vasily Fedosenko
Creating an integrated strategy
We have been exploring these issues for years as part of our ongoing discussions on how to remake labor for the 21st century. In our recently published book, “Shaping the Future of Work: A Handbook for Change and a New Social Contract,” we describe why society needs an integrated strategy to gain control over how future technologies will affect work.
And that strategy starts with helping define the problems humans want new technologies to solve. We shouldn’t be leaving this solely to their inventors.
Fortunately, some engineers and AI experts are recognizing that the end users of a new technology must have a central role in guiding its design to specify which problems they’re trying to solve.
The second step is ensuring that these technologies are designed alongside the work systems with which they will be paired. A so-called simultaneous design process produces better results for both the companies and their workers compared with a sequential strategy – typical today – which involves designing a technology and only later considering the impact on a workforce.
An excellent illustration of simultaneous design is how Toyota handled the introduction of robotics onto its assembly lines in the 1980s. Unlike rivals such as General Motors that followed a sequential strategy, the Japanese automaker redesigned its work systems at the same time, which allowed it to get the most out of the new technologies and its employees. Importantly, Toyota solicited ideas for improving operations directly from workers.
In doing so, Toyota achieved higher productivity and quality in its plants than competitors like GM that invested heavily in stand-alone automation before they began to alter work systems.
Similarly, businesses that tweaked their work systems in concert with investing in IT in the 1990s outperformed those that didn’t. And health care companies like Kaiser Permanente and others learned the same lesson as they introduced electronic medical records over the past decade.
Each example demonstrates that the introduction of a new technology does more than just eliminate jobs. If managed well, it can change how work is done in ways that can both increase productivity and the level of service by augmenting the tasks humans do.
Reuters/Yuriko Nakao
Worker wisdom
But the process doesn’t end there. Companies need to invest in continuous training so their workers are ready to help influence, use and adapt to technological changes. That’s the third step in getting the most out of new technologies.
And it needs to begin before they are introduced. The important part of this is that workers need to learn what some are calling “hybrid” skills: a combination of technical knowledge of the new technology with aptitudes for communications and problem-solving.
Companies whose workers have these skills will have the best chance of getting the biggest return on their technology investments. It is not surprising that these hybrid skills are now in high and growing demand and command good salaries.
None of this is to deny that some jobs will be eliminated and some workers will be displaced. So the final element of an integrated strategy must be to help those displaced find new jobs and compensate those unable to do so for the losses endured. Ford and the United Auto Workers, for example, offered generous early retirement benefits and cash severance payments in addition to retraining assistance when the company downsized from 2007 to 2010.
Examples like this will need to become the norm in the years ahead. Failure to treat displaced workers equitably will only widen the gaps between winners and losers in the future economy that are now already all too apparent.
In sum, companies that engage their workforce when they design and implement new technologies will be best-positioned to manage the coming AI revolution. By respecting the fact that today’s workers, like those before them, understand their jobs better than anyone and the many tasks they entail, they will be better able to “give wisdom to the machines.”
| 2017-08-30T00:00:00 |
2017/08/30
|
https://theconversation.com/robots-wont-steal-our-jobs-if-we-put-workers-at-center-of-ai-revolution-82474
|
[
{
"date": "2017/08/30",
"position": 64,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 61,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 62,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 67,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 63,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 65,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 64,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 69,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 67,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 68,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 66,
"query": "robotics job displacement"
},
{
"date": "2017/08/30",
"position": 75,
"query": "robotics job displacement"
}
] |
The Impact of Automation on Employment - Part I
|
The Impact of Automation on Employment
|
https://www.ncci.com
|
[] |
Over the same period, US manufacturing labor productivity grew 140.1% III. Automation in manufacturing has decreased production costs, making US ...
|
Page Content
Automation has the potential to transform future jobs and the structure of the labor force. As we discussed in the March edition of the QEB, automation in manufacturing has steadily decreased costs for decades, making US manufactures more competitive while also reducing the amount of labor required to produce them. Looking forward, technical advances in computing power, artificial intelligence, and robotics have created the potential for automation to penetrate deeply into occupations beyond manufacturing. The prospect that future automation might transform jobs and the labor force on a systemic scale raises some important questions for workers compensation:
What jobs are more susceptible to automation and what jobs are less susceptible?
How is the composition of employment across economic sectors likely to change?
What happens to workers displaced from occupations impacted by automation?
Because of the breadth of this topic, we will present our analysis in two parts. This edition of the QEB provides historical context for automation and explains why automation in the future has the potential to change labor markets more dramatically than in the past. We review several recent studies that quantify the potential for automation expansion across occupations and economic sectors, and address the question of which jobs are most susceptible to automation. In a follow-up piece to be published later, we will present scenarios for automation penetration across different economic sectors comprising the US economy, from which we will address the questions above concerning changes in the composition of the labor force and what could happen to workers displaced by automation.
AUTOMATION HISTORICALLY
Automation is not a new occurrence, and has contributed to significant shifts in employment throughout the 20th century. With the widespread adoption of the gas-powered tractor in the early 20th century, farmers experienced many benefits including the more efficient use of labor and increased productionI. This contributed to a decline in agricultural employment from close to 40% of total employment in 1900 to less than 2% since 2000, as shown in Figure 1.
Likewise, manufacturing’s share of employment has also declined from a peak of over 25% in the 1950s to less than 10% currently. Much of this decline is due to automation. A Ball State University study found that 87% of the job losses in manufacturing from 2000 to 2010 were due to automation, while 13% were due to globalization and tradeII.
Automation has also contributed to an increase in output, as seen in Figure 2. Since 1990, manufacturing output grew 71.8% while manufacturing employment fell 30.7%. This means that in 2016 the United States produced almost 72% more goods than in 1990, but with only about 70% of the workers. Over the same period, US manufacturing labor productivity grew 140.1%III. Automation in manufacturing has decreased production costs, making US manufactures less expensive and more competitive by reducing the amount of labor required to produce them.
Notwithstanding these dramatic impacts, only a small portion of manufacturing tasks are currently automated. By one estimate, only about 10% of manufacturing tasks globally were performed by robots in 2015. But that percentage is expected to rise to 25% by 2025 as robots become less expensive and easier to program, making them more accessible, particularly to small factoriesIV.
AUTOMATION PENETRATION ACROSS SECTORS
Automation is starting to take hold in other sectors beyond manufacturing. Some well-known examples include kiosks and tablets to place orders and pay in restaurants, robots to process packages in warehouses, and self-driving trucks in transportationV.
Until recently, automation displaced routine tasks that were predictable and could be easily programmed. This included assembly line robots in factories and computers to replace certain occupations such as switchboard operators. Today, advances in artificial intelligence and machine learning enable software to detect patterns in data, allowing some nonroutine tasks and judgmental decisions to be automated. Combined with advances in mobile robotics, machine learning can even permit nonroutine manual tasks to be automated—tasks that could only be performed by humans in the past.
Two recent major studies have attempted to identify which tasks are most susceptible to automation with current technology: "A Future That Works: Automation, Employment and Productivity," by the McKinsey Global InstituteVI; and "The Future of Employment: How Susceptible Are Jobs to Computerisation?" by Carl Frey and Michael Osborne at the University of OxfordVII. We discuss the findings of these studies in the following two sections. Subsequent sections contain a brief survey of related studies by other authors, and a discussion of how the labor market impacts envisioned by recent research on automation differs from more conventional labor market forecasts, such as those published by the US Bureau of Labor Statistics.
MCKINSEY STUDY
Using data from the US Department of Labor for 800 occupations, the McKinsey study identified 2,000 distinct work activities. Each work activity requires some combination of 18 performance capabilities, which come from five groups: sensory perception, cognitive capabilities, natural language processing, social and emotional capabilities, and physical capabilities. For each occupation and activity, McKinsey determined which performance capabilities are demanded, and whether the required level of performance is below, at, or above a median level of human performance. McKinsey then rated the potential for existing technology to substitute for humans in each performance capability in each occupation and activity.
Finally, McKinsey aggregated its 2,000 work activities into seven broad categories. Table 1 shows the average percentage of time spent on each activity category across all occupations, as well as its potential for automation. For example, a 69% automation potential for processing data means that over two-thirds of the time currently spent on this activity by human workers across all occupations might be saved by automation with existing technology.
McKinsey estimates that only 7% of time is spent managing and developing people, and that this is the work activity with the lowest automation potential of 9%. The next three work activities—applying expertise, interfacing, and performing unpredictable physical activities—make up 42% of time, with automation potentials ranging from 18% to 26%. The three remaining categories—collecting data, processing data, and performing predictable physical activities—comprise 51% of all work activities and have high automation potentials ranging from 64% to 81%.
Figure 3 presents McKinsey’s estimates of automation potential by economic sector. Automation potential in an economic sector depends on the shares of time spent on the different work activity categories from Table 1. Circle sizes indicate the amounts of time spent on each work activity, while their colors indicate the automation potential for that activity in the indicated sector. The bar at the right indicates the overall automation potential for the associated sector.
With automation potential of 73%, accommodation and food services is the sector most susceptible to automation with existing technology. A large percentage of work time in this sector is spent on predictable physical activities, the activity category with highest automation potential. Manufacturing and agriculture rank next highest, suggesting that historical automation trends in these sectors are likely to continue. Least susceptible to automation are jobs relating to healthcare, information, professional and management services, and education. These jobs demand higher shares of time spent on managing and developing people and applying expertise to decision making, the two activity categories with the lowest automation potential.
McKinsey’s estimates indicate that the potential for workplace automation depends on the type of occupation. While fewer than 5% of all occupations might be fully automated, about 60% of occupations could have at least 30% of their activities automated. In McKinsey’s baseline scenario, 49% of all of today’s work activities may be automated using currently available technology by 2055. However, McKinsey throws a wide confidence band around this timeline by suggesting that the same degree of automation penetration might occur 20 years earlier in 2035, or 20 years later in 2075. Clearly, estimating the technical potential for automation is one thing, but estimating the speed with which automation occurs is another. McKinsey notes that numerous factors are likely to impact the timing and extent of automation penetration in different occupations, including technical complications in specific job applications, capital investment necessary to develop and deploy solutions, labor costs including benefits and liabilities, and regulatory and social acceptance.
OXFORD STUDY
The Oxford study is based on descriptions of specific tasks for 702 different occupations from the US Department of Labor. Using these descriptions and working with machine learning experts, authors Frey and Osborne identified 70 occupations that they consider to be either entirely automatable or entirely nonautomatable. In doing so, they considered the degree to which each occupation requires any of three bottlenecks to automation: perception and manipulation, creative intelligence, and social intelligence. Perception and manipulation includes finger dexterity, manual dexterity, and awkward positions; creative intelligence includes originality and fine arts; and social intelligence includes social perceptiveness, negotiation, persuasion, and assisting and caring for others. Finally, using these 70 occupations as a basis, Frey and Osborne applied a classification algorithm to assign a degree of automation potential between zero and one to each of the remaining 632 occupations.
Their results suggest that 47% of total US employment is in occupations at high risk for automation (probabilities greater than 70%), while 19% of employment is in occupations at medium risk (probabilities between 30% and 70%), and 33% of employment is in occupations at low risk for automation (probabilities less than 30%). Categories of occupations comprising the highest shares of employment in high- and low-risk automation groups are listed in Table 2.
Although they use different approaches, the Oxford and McKinsey studies reach similar conclusions. Occupations with higher (or lower) automation risk in the Oxford study are exactly those that are typical in the economic sectors identified by the McKinsey study as having similar degrees of automation risk. Economy-wide estimates of automation potential are also similar for both studies: the McKinsey study estimates that 49% of today’s work activities could be automated with currently available technology; the Oxford study concludes that 47% of US employment is in occupations at high risk for automation.
Like the McKinsey study, the Oxford study makes no definite predictions about a timeline for automation, but emphasizes that the speed of automation in various occupations will depend on a variety of factors. Among these, the Oxford study points to the difficulty of overcoming automation bottlenecks like perception and social intelligence, resistance from stakeholders, and the level of wages relative to the cost of automation. Both studies conclude that industries or occupations with a higher potential for automation are likely to be impacted sooner than those with lower potential.
ADDITIONAL STUDIES
This section contains a survey of additional research on automation, some of which extends results from the McKinsey and Oxford studies.
Citigroup . VIII The Citigroup study extends the Oxford estimates both globally and regionally. Using World Bank data for over 50 countries, Citigroup found that the average share of employment at high risk for automation across Organization for Economic Cooperation and Development (OECD) countries is 57%, with shares as high as 69% in India and 77% in China. Citigroup also estimates the share of employment at high risk of automation for US cities: Fresno, CA and Las Vegas, NV had the highest shares of employment at risk of automation, 53.8% and 49.1%, respectively. Cities at high automation risk tend to specialize in industries such as services, sales, office support, and manufacturing. These also tend to be cities with lower wages. Boston, MA and Washington, DC had the lowest employment shares at risk of automation, 38.4% for both. Cities at lower risk generally have more diversified labor forces and more skilled workers in technological industries.
. The Citigroup study extends the Oxford estimates both globally and regionally. University of Redlands . IX The University of Redlands study combines the Oxford study’s estimates with employment data to identify US metropolitan areas most susceptible to automation. Most US metropolitan areas are at risk of losing over 55% of their current jobs due to automation. Metropolitan areas Las Vegas, NV; El Paso TX; and Riverside-San Bernardino, CA have more than 63% of their jobs at risk of automation. These areas have concentrations of lower wage jobs relating to office support, food preparation and serving, sales, and transportation and material moving. High-tech metropolitan areas such as Boston and Silicon Valley have fewer jobs at risk.
. The University of Redlands study combines the Oxford study’s estimates with employment data to identify US metropolitan areas most susceptible to automation. OECD . X This study evaluates the potential for automation for 21 OECD countries. It argues that the McKinsey and Oxford studies may overstate the number of workers at risk from automation for several reasons: First, the OECD stresses the importance of economic, legal, or societal factors that may inhibit the adoption of new technologies. Where the cost of the new technology exceeds the cost of labor, adoption will be slower. Legal questions may slow technology adoption: for example, assigning liability in accidents with a driverless car. People may prefer that certain tasks be performed by humans instead of machines, like nursing. Second, the OECD considers what might happen to human workers in a more automated workplace. As new technology is adopted, workers may switch within their workplaces to more complex tasks that cannot be automated, such as programming and monitoring. New technology related to automation may create jobs or raise wages. Job creation related to production and service of automation tools and technologies. By reducing production costs, automation will lead to higher output in affected industries, ameliorating the displacement effect on workers. Automation which increases worker productivity may also increase wages.
. This study evaluates the potential for automation for 21 OECD countries. It argues that the McKinsey and Oxford studies may overstate the number of workers at risk from automation for several reasons:
DEPARTURE FROM CONVENTIONAL LABOR FORCE FORECASTS
While subject to a lot of conjecture and uncertainty, the McKinsey and Oxford studies point to magnitudes of feasible job automation that go far beyond conventional forecasts for future output and employment in the US economy. Conventional forecasts, like those from the US Bureau of Labor Statistics (BLS) or Moody’s Analytics, are typically based on econometric models that extrapolate historical labor productivity trends into the future. In contrast, the McKinsey and Oxford studies suggest that automation penetration could accelerate dramatically in many economic sectors, with the consequence that labor productivity in those sectors would also jump relative to historical trends.
For example, the most recent output and employment forecast from the BLS projects a cumulative labor productivity gain from 2017 to 2024 of 12% for the US economy excluding government. Over the same period, if automation were to substitute for BLS’s estimate of new jobs at McKinsey’s 49% estimate, then US labor productivity would instead increase 14%.
Similarly, the BLS’s output and employment forecast indicates cumulative productivity gains over the same period of 7% in healthcare and social assistance, and 11% in construction—two sectors with high rates of projected employment growth. If we apply McKinsey’s estimates for technically feasible automation in these sectors to substitute for labor in the BLS’s estimates of new job creation, then the labor productivity gain in healthcare and social assistance would increase to 12%, and in construction to 16%. More automation means that workers in these sectors could become substantially more productive, but also that fewer workers would then be needed to produce a given amount of output or services.
WHAT MIGHT AUTOMATION MEAN FOR THE FUTURE LABOR MARKET?
In a follow-up piece to this QEB, we will present scenarios for automation penetration across economic sectors of interest to workers compensation, which collectively comprise most covered employment in the US. Using McKinsey’s estimates for potential automation penetration in different economic sectors, we will address the following questions at the national level:
What happens to workers in economic sectors impacted by automation? In an industry or sector, automation increases worker productivity, which means that the same amount of output could be produced with fewer workers. However, more productive workers also earn higher wages, and lower production costs mean lower prices and more output demanded, which tends to raise employment. Total employment in a sector will depend on the interplay of these effects.
How might the distribution of employment across economic sectors change as the result of automation? In the national economy, employment shares can be expected to shift from sectors more impacted by automation to other sectors that are less impacted. How might re-equilibration in the labor market affect the overall size of the labor force and the employment shares of different industries and occupations?
FORTHCOMING RESEARCH: SCENARIOS FOR AUTOMATION AND THE FUTURE LABOR MARKET
A follow-up piece will present scenarios for the potential impact of automation on the US labor market, addressing what might happen to workers in economic sectors impacted by automation, and how the distribution of employment across economic sectors may change as a result of automation.
© Copyright 2017 National Council on Compensation Insurance, Inc. All Rights Reserved.
THE RESEARCH ARTICLES AND CONTENT DISTRIBUTED BY NCCI ARE PROVIDED FOR GENERAL INFORMATIONAL PURPOSES ONLY AND ARE PROVIDED “AS IS.” NCCI DOES NOT GUARANTEE THEIR ACCURACY OR COMPLETENESS NOR DOES NCCI ASSUME ANY LIABILITY THAT MAY RESULT IN YOUR RELIANCE UPON SUCH INFORMATION. NCCI EXPRESSLY DISCLAIMS ANY AND ALL WARRANTIES OF ANY KIND INCLUDING ALL EXPRESS, STATUTORY AND IMPLIED WARRANTIES INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
Note: Analysis and charts prepared in May and June 2017.
| 2017-10-10T00:00:00 |
https://www.ncci.com/Articles/Pages/II_Insights_QEB_Impact-Automation-Employment-Q2-2017-Part1.aspx
|
[
{
"date": "2017/10/10",
"position": 57,
"query": "job automation statistics"
},
{
"date": "2017/10/10",
"position": 57,
"query": "job automation statistics"
},
{
"date": "2017/10/10",
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},
{
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{
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},
{
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{
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{
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{
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{
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] |
|
Most US workers want to see more AI and robots in the office | ZDNET
|
Most US workers want to see more AI and robots in the office
|
https://www.zdnet.com
|
[
"Eileen Brown",
"Oct.",
"At A.M. Pt"
] |
Although workers want robots in the workplace to give them more time to do their primary job duties, almost all still want the human touch ...
|
Video: No, robots won't take your job -- just part of it
Currently, workers spend only 44 percent of their time on their primary duties, mainly because email, meetings, and non-essential tasks take up the bulk of their working week.
Cloud-based enterprise work management solutions provider Workfront released a report showing that US workers are largely optimistic about the impact automation will have in the workplace.
Its annual State of Enterprise Work report aims to capture how work is currently being done and what challenges office workers see in the present.
Workfront surveyed 2001 US residents who worked for companies with over 500 employees. These employees worked on computers and collaborated on projects. It wanted to record how workers see current workplace trends playing out in the near future.
The report highlighted three major themes:
Wasteful practices and tools, namely email and meetings, continue to thwart worker productivity. Poorly used meetings and email topped the list of things that prevent knowledge workers from getting work done. US workers have an average of 199 unopened emails in their inboxes at any given time. This indicates that email has reached the limits of its effectiveness as a work management tool.
Flexibility is on the rise. The report shows that 79 percent of knowledge workers now have the ability to use flexible working. Companies are seeing the benefits of allowing their team members to work outside the office and outside standard business hours. The average knowledge worker now works from home for eight hours every week.
Automation is the future. Four out of five knowledge workers see automation as a chance to rethink work in new and exciting ways. Sixty-nine percent believe work automation will give them back time to perform their primary job duties better. The only uncertainty is in how much of work will ultimately be done by machines and how much will still require the human touch
So, while the overwhelming view on automation was positive, one in three (34 percent) feared that "men and women in my line of work will be competing with robots, machines, and/or artificial intelligence." Eighty-six percent of respondents agreed with the sentiment that "the use of automation in the workplace will let us think of work in new and innovative ways," whilst 82 percent expressed excitement at the chance "to learn new things as the workforce moves toward more automation."
Read also: Chuwi SurBook hands-on: A high-performing Surface clone | Mgcool Explorer 1S hands-on: A low-cost action camera with some great features | Hands on with the Destek V4 VR Headset: Great quality for an affordable price
Alex Shootman, president and CEO of Workfront, said: "Popular culture may depict automation in dystopian terms, but the reality is that the majority of workers are optimistic about automation because they understand how it helps them focus on high-value tasks at work.:
Although automation seems like it is poised to take over our lives at home and work, there is hope. More than half or respondents (52 percent) agreed that "no matter how sophisticated artificial intelligence becomes, there will always be the need for the human touch in the workplace."
Even though humans might be just fixing the machines in the fully automated office.
Previous and related coverage
As AI floods the market, which chatbots deliver the best ROI for enterprises?
A recent report shows that AI and chatbots can bring a huge ROI (return on investment) for the enterprise. But which solution should you choose?
Although chatbot use rises, we still prefer talking to humans
Research has forecast that bot interactions in the banking sector, completed without human assistance, will move from 12 percent to over 90 percent in 2022. Will this mean the end of the contact centre agent as bots take over?
Will robots ever really become part of our daily lives?
Tesla CEO Elon Musk sees artificial intelligence as the future. Professor Stephen Hawking thinks robots will one day destroy all human life. Who do you side with on the AI debate?
| 2017-10-24T00:00:00 |
https://www.zdnet.com/article/four-out-of-five-workers-want-to-see-more-ai-and-robots-in-the-office/
|
[
{
"date": "2017/10/24",
"position": 92,
"query": "AI workers"
}
] |
|
How Many Jobs can be Automated? - Medium
|
How Many Jobs can be Automated?
|
https://medium.com
|
[
"Joe Psotka"
] |
Automation of labor is an increasingly important topic to researchers, businesses, and unions. There are so many opinions about how many ...
|
Member-only story How Many Jobs can be Automated? Joe Psotka 6 min read · Oct 27, 2017 -- Share
Automation of labor is an increasingly important topic to researchers, businesses, and unions. There are so many opinions about how many jobs can be automated that range from 10% to 75% so that it is very hard to make any sense of the data. Existing estimates are wide ranging. One of the best studies, Frey and Osborne estimate that 47% of U.S. employment is at high risk of computerization in the foreseeable future, while an alternative OECD study concludes a more modest 9% of employment is at risk. How many of these estimators have any real idea about the capabilities of AI and robots?Very few can have, since the whole field is in rapid transition.
An article by Robert D. Atkinson and John Wu in May of this year pooh - poohs the whole idea of dramatic exponential change in employment by showing that job churn has actually decreased since the last century when agricultural jobs were dramatically disappearing. They argue that this dynamic will NOT change in the future for the simple reason that consumer wants are far from satisfied. This seems a potentially troubling argument, since job loss implies a loss of consumer demand. A future dystopia would leave most consumer desires unsatisfied.
| 2017-10-31T00:00:00 |
2017/10/31
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https://medium.com/@joepsotka/how-many-jobs-can-be-automated-4505220c16e7
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Jobs of the future: Jobs lost, jobs gained - McKinsey & Company
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Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages
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https://www.mckinsey.com
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[
"James Manyika",
"Susan Lund",
"Michael Chui",
"Jacques Bughin",
"Lola Woetzel",
"Parul Batra",
"Ryan Ko",
"Saurabh Sanghvi"
] |
We estimate that between 400 million and 800 million individuals could be displaced by automation and need to find new jobs by 2030 around the ...
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Get the latest
The technology-driven world in which we live is a world filled with promise but also challenges. Cars that drive themselves, machines that read X-rays, and algorithms that respond to customer-service inquiries are all manifestations of powerful new forms of automation. Yet even as these technologies increase productivity and improve our lives, their use will substitute for some work activities humans currently perform—a development that has sparked much public concern.
Building on our January 2017 report on automation, McKinsey Global Institute’s latest report, Jobs lost, jobs gained: Workforce transitions in a time of automation (PDF–5MB), assesses the number and types of jobs that might be created under different scenarios through 2030 and compares that to the jobs that could be lost to automation.
The results reveal a rich mosaic of potential shifts in occupations in the years ahead, with important implications for workforce skills and wages. Our key finding is that while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging—matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.
1. What impact will automation have on work?
We previously found that about half the activities people are paid to do globally could theoretically be automated using currently demonstrated technologies. Very few occupations—less than 5 percent—consist of activities that can be fully automated.
However, in about 60 percent of occupations, at least one-third of the constituent activities could be automated, implying substantial workplace transformations and changes for all workers.
While technical feasibility of automation is important, it is not the only factor that will influence the pace and extent of automation adoption. Other factors include the cost of developing and deploying automation solutions for specific uses in the workplace, the labor-market dynamics (including quality and quantity of labor and associated wages), the benefits of automation beyond labor substitution, and regulatory and social acceptance.
Taking these factors into account, our new research estimates that between almost zero and 30 percent of the hours worked globally could be automated by 2030, depending on the speed of adoption. We mainly use the midpoint of our scenario range, which is automation of 15 percent of current activities. Results differ significantly by country, reflecting the mix of activities currently performed by workers and prevailing wage rates.
The potential impact of automation on employment varies by occupation and sector (see interactive above). Activities most susceptible to automation include physical ones in predictable environments, such as operating machinery and preparing fast food. Collecting and processing data are two other categories of activities that increasingly can be done better and faster with machines. This could displace large amounts of labor—for instance, in mortgage origination, paralegal work, accounting, and back-office transaction processing.
It is important to note, however, that even when some tasks are automated, employment in those occupations may not decline but rather workers may perform new tasks.
Automation will have a lesser effect on jobs that involve managing people, applying expertise, and social interactions, where machines are unable to match human performance for now.
Jobs in unpredictable environments—occupations such as gardeners, plumbers, or providers of child- and eldercare—will also generally see less automation by 2030, because they are technically difficult to automate and often command relatively lower wages, which makes automation a less attractive business proposition.
Section 2
2. What are possible scenarios for employment growth?
Workers displaced by automation are easily identified, while new jobs that are created indirectly from technology are less visible and spread across different sectors and geographies. We model some potential sources of new labor demand that may spur job creation to 2030, even net of automation.
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For the first three trends, we model only a trendline scenario based on current spending and investment trends observed across countries.
Rising incomes and consumption, especially in emerging economies
We have previously estimated that global consumption could grow by $23 trillion between 2015 and 2030, and most of this will come from the consuming classes in emerging economies. The effects of these new consumers will be felt not just in the countries where the income is generated but also in economies that export to these countries. Globally, we estimate that 250 million to 280 million new jobs could be created from the impact of rising incomes on consumer goods alone, with up to an additional 50 million to 85 million jobs generated from higher health and education spending.
Aging populations
By 2030, there will be at least 300 million more people aged 65 years and older than there were in 2014. As people age, their spending patterns shift, with a pronounced increase in spending on healthcare and other personal services. This will create significant new demand for a range of occupations, including doctors, nurses, and health technicians but also home-health aides, personal-care aides, and nursing assistants in many countries. Globally, we estimate that healthcare and related jobs from aging could grow by 50 million to 85 million by 2030.
Development and deployment of technology
Jobs related to developing and deploying new technologies may also grow. Overall spending on technology could increase by more than 50 percent between 2015 and 2030. About half would be on information-technology services. The number of people employed in these occupations is small compared to those in healthcare or construction, but they are high-wage occupations. By 2030, we estimate that this trend could create 20 million to 50 million jobs globally.
For the next three trends, we model both a trendline scenario and a step-up scenario that assumes additional investments in some areas, based on explicit choices by governments, business leaders, and individuals to create additional jobs.
Investments in infrastructure and buildings
Infrastructure and buildings are two areas of historic underspending that may create significant additional labor demand if action is taken to bridge infrastructure gaps and overcome housing shortages. New demand could be created for up to 80 million jobs in the trendline scenario and, in the event of accelerated investment, up to 200 million more in the step-up scenario. These jobs include architects, engineers, electricians, carpenters, and other skilled tradespeople, as well as construction workers.
Investments in renewable energy, energy efficiency, and climate adaptation
Investments in renewable energy, such as wind and solar; energy-efficiency technologies; and adaptation and mitigation of climate change may create new demand for workers in a range of occupations, including manufacturing, construction, and installation. These investments could create up to ten million new jobs in the trendline scenario and up to ten million additional jobs globally in the step-up scenario.
‘Marketization’ of previously unpaid domestic work
The last trend we consider is the potential to pay for services that substitute for currently unpaid and primarily domestic work. This so-called marketization of previously unpaid work is already prevalent in advanced economies, and rising female workforce participation worldwide could accelerate the trend. We estimate that this could create 50 million to 90 million jobs globally, mainly in occupations such as childcare, early-childhood education, cleaning, cooking, and gardening.
When we look at the net changes in job growth across all countries, the categories with the highest percentage job growth net of automation include the following:
healthcare providers
professionals such as engineers, scientists, accountants, and analysts
IT professionals and other technology specialists
managers and executives, whose work cannot easily be replaced by machines
educators, especially in emerging economies with young populations
“creatives,” a small but growing category of artists, performers, and entertainers who will be in demand as rising incomes create more demand for leisure and recreation
builders and related professions, particularly in the scenario that involves higher investments in infrastructure and buildings
manual and service jobs in unpredictable environments, such as home-health aides and gardeners
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Upcoming workforce transitions could be very large
The changes in net occupational growth or decline imply that a very large number of people may need to shift occupational categories and learn new skills in the years ahead. The shift could be on a scale not seen since the transition of the labor force out of agriculture in the early 1900s in the United States and Europe, and more recently in in China.
Seventy-five million to 375 million may need to switch occupational categories and learn new skills.
We estimate that between 400 million and 800 million individuals could be displaced by automation and need to find new jobs by 2030 around the world, based on our midpoint and earliest (that is, the most rapid) automation adoption scenarios. New jobs will be available, based on our scenarios of future labor demand and the net impact of automation, as described in the next section.
However, people will need to find their way into these jobs. Of the total displaced, 75 million to 375 million may need to switch occupational categories and learn new skills, under our midpoint and earliest automation adoption scenarios; under our trendline adoption scenario, however, this number would be very small—less than 10 million (Exhibit 1).
In absolute terms, China faces the largest number of workers needing to switch occupations—up to 100 million if automation is adopted rapidly, or 12 percent of the 2030 workforce. While that may seem like a large number, it is relatively small compared with the tens of millions of Chinese who have moved out of agriculture in the past 25 years.
For advanced economies, the share of the workforce that may need to learn new skills and find work in new occupations is much higher: up to one-third of the 2030 workforce in the United States and Germany, and nearly half in Japan.
Section 3
3. Will there be enough work in the future?
Today there is a growing concern about whether there will be enough jobs for workers, given potential automation. History would suggest that such fears may be unfounded: over time, labor markets adjust to changes in demand for workers from technological disruptions, although at times with depressed real wages (Exhibit 2).
We address this question about the future of work through two different sets of analyses: one based on modeling of a limited number of catalysts of new labor demand and automation described earlier, and one using a macroeconomic model of the economy that incorporates the dynamic interactions among variables.
If history is any guide, we could also expect that 8 to 9 percent of 2030 labor demand will be in new types of occupations that have not existed before.
Both analyses lead us to conclude that, with sufficient economic growth, innovation, and investment, there can be enough new job creation to offset the impact of automation, although in some advanced economies additional investments will be needed as per our step-up scenario to reduce the risk of job shortages.
A larger challenge will be ensuring that workers have the skills and support needed to transition to new jobs. Countries that fail to manage this transition could see rising unemployment and depressed wages.
The magnitude of future job creation from the trends described previously and the impact of automation on the workforce vary significantly by country, depending on four factors.
Wage level
Higher wages make the business case for automation adoption stronger. However, low-wage countries may be affected as well, if companies adopt automation to boost quality, achieve tighter production control, move production closer to end consumers in high-wage countries, or other benefits beyond reducing labor costs.
Demand growth
Economic growth is essential for job creation; economies that are stagnant or growing slowly create few if any net new jobs. Countries with stronger economic and productivity growth and innovation will therefore be expected to experience more new labor demand.
Demographics
Countries with a rapidly growing workforce, such as India, may enjoy a “demographic dividend” that boosts GDP growth—if young people are employed. Countries with a shrinking workforce, such as Japan, can expect lower future GDP growth, derived only from productivity growth.
Mix of economic sectors and occupations
The automation potential for countries reflects the mix of economic sectors and the mix of jobs within each sector. Japan, for example, has a higher automation potential than the United States because the weight of sectors that are highly automatable, such as manufacturing, is higher.
Automation will affect countries in different ways
The four factors just described combine to create different outlooks for the future of work in each country (see interactive heat map). Japan is rich, but its economy is projected to grow slowly to 2030. It faces the combination of slower job creation coming from economic expansion and a large share of work that can be automated as a result of high wages and the structure of its economy.
However, Japan will also see its workforce shrink by 2030 by four million people. In the step-up scenario, and considering the jobs in new occupations we cannot envision today, Japan’s net change in jobs could be roughly in balance.
The United States and Germany could also face significant workforce displacement from automation by 2030, but their projected future growth—and hence new job creation—is higher. The United States has a growing workforce, and in the step-up scenario, with innovations leading to new types of occupations and work, it is roughly in balance. Germany’s workforce will decline by three million people by 2030, and it will have more than enough labor demand to employ all its workers, even in the trendline scenario.
At the other extreme is India: a fast-growing developing country with relatively modest potential for automation over the next 15 years, reflecting low wage rates. Our analysis finds that most occupational categories are projected to grow in India, reflecting its potential for strong economic expansion.
However, India’s labor force is expected to grow by 138 million people by 2030, or about 30 percent. India could create enough new jobs to offset automation and employ these new entrants by undertaking the investments in our step-up scenario.
China and Mexico have higher wages than India and so are likely to see more automation. China is still projected to have robust economic growth and will have a shrinking workforce; like Germany, China’s problem could be a shortage of workers.
Mexico’s projected rate of future economic expansion is more modest, and it could benefit from the job creation in the step-up scenario plus innovation in new occupations and activities to make full use of its workforce.
Displaced workers will need to be reemployed quickly to avoid rising unemployment
To model the impact of automation on overall employment and wages, we use a general equilibrium model that takes into account the economic impacts of automation and dynamic interactions. Automation has at least three distinct economic impacts. Most attention has been devoted to the potential displacement of labor. But automation also may raise labor productivity: firms adopt automation only when doing so enables them to produce more or higher-quality output with the same or fewer inputs (including material, energy, and labor inputs). The third impact is that automation adoption raises investment in the economy, lifting short-term GDP growth. We model all three effects. We also create different scenarios for how quickly displaced workers find new employment, based on historical data.
The results reveal that, in nearly all scenarios, the six countries that are the focus of our report (China, Germany, India, Japan, Mexico, and the United States) could expect to be at or very near full employment by 2030. However, the model also illustrates the importance of reemploying displaced workers quickly.
If displaced workers are able to be reemployed within one year, our model shows automation lifting the overall economy: full employment is maintained in both the short and long term, wages grow faster than in the baseline model, and productivity is higher.
However, in scenarios in which some displaced workers take years to find new work, unemployment rises in the short to medium term. The labor market adjusts over time and unemployment falls—but with slower average wage growth. In these scenarios, average wages end up lower in 2030 than in the baseline model, which could dampen aggregate demand and long-term growth.
Section 4
4. What will automation mean for skills and wages?
In general, the current educational requirements of the occupations that may grow are higher than those for the jobs displaced by automation. In advanced economies, occupations that currently require only a secondary education or less see a net decline from automation, while those occupations requiring college degrees and higher grow.
In India and other emerging economies, we find higher labor demand for all education levels, with the largest number of new jobs in occupations requiring a secondary education, but the fastest rate of job growth will be for occupations currently requiring a college or advanced degree.
Workers of the future will spend more time on activities that machines are less capable of, such as managing people, applying expertise, and communicating with others. They will spend less time on predictable physical activities and on collecting and processing data, where machines already exceed human performance. The skills and capabilities required will also shift, requiring more social and emotional skills and more advanced cognitive capabilities, such as logical reasoning and creativity.
Wages may stagnate or fall in declining occupations. Although we do not model shifts in relative wages across occupations, the basic economics of labor supply and demand suggests that this should be the case for occupations in which labor demand declines.
Our analysis shows that most job growth in the United States and other advanced economies will be in occupations currently at the high end of the wage distribution. Some occupations that are currently low wage, such as nursing assistants and teaching assistants, will also increase, while a wide range of middle-income occupations will have the largest employment declines.
Income polarization could continue. Policy choices such as increasing investments in infrastructure, buildings, and energy transitions could help create additional demand for middle-wage jobs such as construction workers in advanced economies.
The wage-trend picture is quite different in emerging economies such as China and India, where our scenarios show that middle-wage jobs such as retail salespeople and teachers will grow the most as these economies develop. This implies that their consuming class will continue to grow in the decades ahead.
section 5
5. How do we manage the upcoming workforce transitions?
The benefits of artificial intelligence and automation to users and businesses, and the economic growth that could come via their productivity contributions, are compelling. They will not only contribute to dynamic economies that create jobs but also help create the economic surpluses that will enable societies to address the workforce transitions that will likely happen regardless.
Faced with the scale of worker transitions we have described, one reaction could be to try to slow the pace and scope of adoption in an attempt to preserve the status quo. But this would be a mistake. Although slower adoption might limit the scale of workforce transitions, it would curtail the contributions that these technologies make to business dynamism and economic growth. We should embrace these technologies but also address the workforce transitions and challenges they bring. In many countries, this may require an initiative on the scale of the Marshall Plan, involving sustained investment, new training models, programs to ease worker transitions, income support, and collaboration between the public and private sectors.
All societies will need to address four key areas.
Maintaining robust economic growth to support job creation
Sustaining robust aggregate demand growth is critical to support new job creation, as is support for new business formation and innovation. Fiscal and monetary policies that ensure sufficient aggregate demand, as well as support for business investment and innovation, will be essential. Targeted initiatives in certain sectors could also help, including, for example, increasing investments in infrastructure and energy transitions.
Scaling and reimagining job retraining and workforce skills development
Providing job retraining and enabling individuals to learn marketable new skills throughout their lifetime will be a critical challenge—and for some countries, the central challenge. Midcareer retraining will become ever more important as the skill mix needed for a successful career changes. Business can take a lead in some areas, including with on-the-job training and providing opportunities to workers to upgrade their skills.
Improving business and labor-market dynamism, including mobility
Greater fluidity will be needed in the labor market to manage the difficult transitions we anticipate. This includes restoring now-waning labor mobility in advanced economies. Digital talent platforms can foster fluidity, by matching workers and companies seeking their skills and by providing a plethora of new work opportunities for those open to taking them. Policy makers in countries with inflexible labor markets can learn from others that have deregulated, such as Germany, which transformed its federal unemployment agency into a powerful job-matching entity.
Providing income and transition support to workers
Income support and other forms of transition assistance to help displaced workers find gainful employment will be essential. Beyond retraining, a range of policies can help, including unemployment insurance, public assistance in finding work, and portable benefits that follow workers between jobs.
We know from history that wages for many occupations can be depressed for some time during workforce transitions. More permanent policies to supplement work incomes might be needed to support aggregate demand and ensure societal fairness. More comprehensive minimum-wage policies, universal basic income, or wage gains tied to productivity growth are all possible solutions being explored.
Policy makers, business leaders, and individual workers all have constructive and important roles to play in smoothing workforce transitions ahead. History shows us that societies across the globe, when faced with monumental challenges, often rise to the occasion for the well-being of their citizens.
Yet over the past few decades, investments and policies to support the workforce have eroded. Public spending on labor-force training and support has fallen in most member countries of the Organisation for Economic Co-operation and Development (OECD). Educational models have not fundamentally changed in 100 years. It is now critical to reverse these trends, with governments making workforce transitions and job creation a more urgent priority.
We will all need creative visions for how our lives are organized and valued in the future, in a world where the role and meaning of work start to shift.
Businesses will be on the front lines of the workplace as it changes. This will require them to both retool their business processes and reevaluate their talent strategies and workforce needs, carefully considering which individuals are needed, which can be redeployed to other jobs, and where new talent may be required. Many companies are finding it is in their self-interest—as well as part of their societal responsibility—to train and prepare workers for a new world of work.
Individuals, too, will need to be prepared for a rapidly evolving future of work. Acquiring new skills that are in demand and resetting intuition about the world of work will be critical for their own well-being. There will be demand for human labor, but workers everywhere will need to rethink traditional notions of where they work, how they work, and what talents and capabilities they bring to that work.
| 2017-11-28T00:00:00 |
https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages
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] |
|
Automation Could Eliminate 73 Million U.S. Jobs By 2030 - Statista
|
Chart: Automation Could Eliminate 73 Million U.S. Jobs By 2030
|
https://www.statista.com
|
[
"Niall Mccarthy",
"Felix Richter"
] |
Midpoint automation could lead to 39 million U.S. job losses by 2030 while rapid automation could cost 73 million. Despite the potential losses, ...
|
HTML code to embed chart
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| 2017-12-01T00:00:00 |
https://www.statista.com/chart/12082/automation-could-eliminate-73-million-us-jobs-by-2030/
|
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|
AI's global impact on job creation and elimination 2022 - Statista
|
AI's global impact on job creation and elimination 2022
|
https://www.statista.com
|
[
"Bergur Thormundsson",
"Jun"
] |
This statistic shows an estimate of the number of jobs created and eliminated by artificial intelligence (AI) worldwide, as of 2022.
|
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Gartner. (December 13, 2017). The number of jobs created and eliminated due to artificial intelligence (AI) worldwide in 2022* (in millions) [Graph]. In Statista . Retrieved July 14, 2025, from https://www.statista.com/statistics/791992/worldwide-jobs-creation-elimination-due-to-ai/
Gartner. "The number of jobs created and eliminated due to artificial intelligence (AI) worldwide in 2022* (in millions)." Chart. December 13, 2017. Statista. Accessed July 14, 2025. https://www.statista.com/statistics/791992/worldwide-jobs-creation-elimination-due-to-ai/
Gartner. (2017). The number of jobs created and eliminated due to artificial intelligence (AI) worldwide in 2022* (in millions) . Statista . Statista Inc.. Accessed: July 14, 2025. https://www.statista.com/statistics/791992/worldwide-jobs-creation-elimination-due-to-ai/
Gartner. "The Number of Jobs Created and Eliminated Due to Artificial Intelligence (Ai) Worldwide in 2022* (in Millions)." Statista , Statista Inc., 13 Dec 2017, https://www.statista.com/statistics/791992/worldwide-jobs-creation-elimination-due-to-ai/
Gartner, The number of jobs created and eliminated due to artificial intelligence (AI) worldwide in 2022* (in millions) Statista, https://www.statista.com/statistics/791992/worldwide-jobs-creation-elimination-due-to-ai/ (last visited July 14, 2025)
The number of jobs created and eliminated due to artificial intelligence (AI) worldwide in 2022* (in millions) [Graph], Gartner, December 13, 2017. [Online]. Available: https://www.statista.com/statistics/791992/worldwide-jobs-creation-elimination-due-to-ai/
| 2017-12-13T00:00:00 |
https://www.statista.com/statistics/791992/worldwide-jobs-creation-elimination-due-to-ai/
|
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|
What can machine learning do? Workforce implications - PubMed
|
What can machine learning do? Workforce implications
|
https://pubmed.ncbi.nlm.nih.gov
|
[
"Brynjolfsson E Mitchell T",
"Zhang Y",
"Liu X.",
"Et Al.",
"Sanderson K",
"Cocker F.",
"No Authors Listed",
"Anthes E.",
"Richards Ba",
"Frankland Pw."
] |
What can machine learning do? Workforce implications · Publication types · MeSH terms. Decision Making; Humans; Machine Learning / statistics & ...
|
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Send even when there aren't any new results
| 2017-12-22T00:00:00 |
https://pubmed.ncbi.nlm.nih.gov/29269459/
|
[
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"query": "machine learning workforce"
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] |
|
What Can Machine Learning Do? Workforce Implications
|
What Can Machine Learning Do? Workforce Implications
|
https://ide.mit.edu
|
[] |
We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and ...
|
MIT IDE Director and Professor, Erik Brynjolfsson, and co-author, Professor Tom Mitchell, of Carnegie Mellon University, have published a new article in Science magazine describing the capabilities–and current limitations– of machine learning, and what it means for the workforce and the economy.
Here is their opening summary:
Digital computers have transformed work in almost every sector of the economy over the past several decades. We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself.
However, although it is clear that ML is a “general purpose technology,” like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities, there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)].
Although parts of many jobs may be “suitable for ML” (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some. Although economic effects of ML are relatively limited today, and we are not facing the imminent “end of work” as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound.
Read the full article here.
The 21-item Rubric to determine Suitability for Machine Learning (SML) can be found here.
| 2017-12-28T00:00:00 |
2017/12/28
|
https://ide.mit.edu/insights/what-can-machine-learning-do-workforce-implications/
|
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7 challenges for AI in journalism - The World Economic Forum
|
7 challenges for AI in journalism
|
https://www.weforum.org
|
[] |
As AI begins to penetrate deeper into journalism and other creative activities, we identified 7 key challenges that need to be addressed.
|
A few weeks ago, Twitter released its latest earnings report. The announcement led to a stock jump for the company, as it appeared to be moving towards profitability. “Twitter is reporting a loss of $21.1 million in its third quarter, but turned in a better-than-expected profit when one-time charges and benefits are removed”, one commentator wrote. “Shares of Twitter Inc. soared almost 9 percent before the opening bell Thursday.”
The author of this passage was, in fact, an artificial intelligence (AI) programme called WordSmith, which turns structured data into a compelling text indistinguishable from one written by a human author.
The advances of AI in journalism are just one element of the rapidly-approaching breakthrough in the technology. AI is getting better at a range of tasks, including many areas thought to be the province of human beings, as illustrated by the infographic below. The AI industry is expected to expand by a compound annual growth rate (CAGR) of 50%f rom 2015-25, and is on course to be worth $127 billion by 2025. “AI is set to be the largest driver of tech spend over the next decade,” Sarbjit Nahal, Managing Director at Bank of America Merrill Lynch.
In the process of this growth, many creative industries will be automated to some extent by AI, because their value chains work in similar ways. They each start with content creation or collection, move on to processing and editing, and end in distribution. And once AI grasps the mechanics of one creative process, it can quickly be applied to another.
Journalism is one sector within the creative economy that has adopted AI into its creation process. It provides a paradigm as to how the technology may develop in other creative activities. Across this value chain, there are three ways that AI is changing the practice of journalism.
1. Automating routine reporting: The use of AI in journalism has helped to rapidly expand coverage; the Associated Press was able to expand the number of companies it reported on from 300 to 4,000 using AI to generate reports on corporate earnings. A Stanford study found evidence that the additional companies reported on experienced increased trading activity and market liquidity, thereby strengthening the market.
Elsewhere in the industry, the co-founder of NarrativeScience predicts up to 90% of articles will be written by AI within 15 years. Similar technology is available to summarise long articles into bite-sized content for social media. The technology can save journalists time, freeing them up to conduct interviews with real people.
“Narratives are just another form of data visualization. Look at a young sports reporter; he would say he’s not scared. He can have high school baseball stories written by AI, so he can go out and do more interesting things,” says Jeff Jarvis, founder of BuzzMachine.
2. Providing faster insight: AI has the ability to instantaneously react to real-time data with the outlines of a story. Quarterly reports, such as performance and attribution reports released by large mutual funds, used to take weeks of effort by a small team of portfolio managers to draft. These reports can now be prepared by AI in a matter of seconds.
Reuters, one of the largest news providers, has partnered with Graphiq, a service that uses AI to build and update data visualizations. The tool enables faster access to data, and, once they are embedded in a news story, the visualizations are updated in real time.
3. Lowering barriers to entry: Finally, AI can reduce the human element in the content creation process—in fact AI is being used today to allow journalists to create short videos from text in seconds or collect information from on-the-ground sources. However, this does not eliminate the need for reporters. Harnessed properly, AI will allow local and regional news companies to create compelling content in order to compete with large newsrooms. If the quality of a news piece depend less on the skills or experience of a single reporter, today’s biggest news companies could lose some audience share to a few dynamic upstarts that are able to use AI effectively.
“The market is oversupplied with content, and the only way to compete is to deliver more and better at the same time. Publishers can get volume through automation, and quality through augmentation, which can help distinguish content from other providers,” according to Francesco Marconi, AP’s co-lead on AI.
The 7 challenges of creative AI
As AI begins to penetrate deeper into journalism and other creative activities, we identified 7 key challenges that need to be addressed:
Technical challenges
1. Availability of data: Machine learning works best when there is sufficient data for it to pick up on patterns, learn from them and optimize the system accordingly. While human beings can analogize experiences and intuit optimal responses through just a few similar experiences, AI requires large amounts of data to know what the correct response ought to be. Without the availability of data, the ability of AI is limited. “For deep learning to work well, you may need millions of data points,” says Takuya Kitagawa, Chief Data Strategy Officer at Rakuten.
2. Understanding unstructured data: AI also has difficulty with unstructured data. Tabulated results of sports games or earnings data can be easily translated into articles by using standardized templates, but if AI is to become more widespread in the creative economy, it will need to harness and synthesize unstructured data, which makes up most of the data available today.
3. Lack of self-awareness: AI is unable to explain its output: why it wrote what it did, or how it got there. In order to understand how data translates into a particular story, designing AI to be accountable to consumers will need to be thoughtfully considered. One way to achieve this – especially with data-heavy content where AI has a natural advantage – might be by allowing consumers to adjust the parameters of an algorithm to see how the results change. When applied to news, it is worth considering whether new journalistic standards need to be developed so that users know whether a story was authored by a machine or human.
4. Verifying authenticity: AI cannot distinguish whether the input it receives is accurate or inaccurate. This can lead to issues around authenticity – if AI receives questionable input, the corresponding output may be false. The solution is to develop and implement mechanisms to ensure the authenticity of articles that are shared online. This could include metadata and trackbacks for facts and sources.
Governance challenges
5. Redefining copyright and fair use: New technologies have frequently challenged copyright laws in the creative industries. Machine learning potentially poses a new conflict, because it involves AI learning from human-created ‘expressive’ works – a data set of articles, paintings or music, for example, that tend to have rights owners – and generating its own output. This is likely to test the legal interpretation of ‘fair use’, where copyrighted material is used to produce new and ‘transformative’ content without permission or payment of royalties.
6. Ensuring corporate accountability: Since AI cannot be held legally accountable, human accountability needs to be embedded in all stages of the content value chain. Content distributers such as Facebook, Google and Twitter hold unparalleled power to inform and shape public opinion, because AI algorithms are used to determine the relative value of content that appears in front of users. The platforms as owners of the means of production therefore have a responsibility to prevent the dissemination and promotion of damaging information by the algorithms they have developed. While better measures are needed to ensure that intentionally misleading content is stopped at the root, one might ask whether false news and disinformation persist because the commercial incentives to increase engagement are too great to resist.
7. Exacerbating asymmetrical power: The biggest newsrooms are building their own AI, but smaller outlets may not have the financial ability or technical expertise, and would be forced to license proprietary content. The fear is that choosing to ‘buy’ rather than ‘build’ fuels an ‘arms race for AI’ that consolidates power amongst a handful of companies. A similar dynamic may emerge elsewhere in the creative economy as AI becomes more widespread – in the same way that advertising has become more reliant on tech giants for revenue growth, other creative industries may see their operating infrastructure designed by Silicon Valley.
Despite the challenges, The World Economic Forum is optimistic about the development of this technology. However, a pessimist would be justified in asking whether we should completely trust the industry to regulate it. Organizations like OpenAI argue that the “industry is investing such vast sums of money into AI research that commercial, private entities were on track to create the first powerful AI systems, and these entities don’t have a built-in mechanism to ensure that everyone benefits from advances”.
The System Initiative on the Future of Information and Entertainment will continue exploring the role of AI in other creative industries in our upcoming research, which will be published on our Mastering a New Reality project home page in coming months. For now, this article has been brought to you by a mere mortal.
This is part of a series of articles related to the disruptive effects of several technologies (virtual/augmented reality, artificial intelligence and blockchain) on the creative economy.
| 2018-01-15T00:00:00 |
https://www.weforum.org/stories/2018/01/can-you-tell-if-this-article-was-written-by-a-robot-7-challenges-for-ai-in-journalism/
|
[
{
"date": "2018/01/15",
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"date": "2018/01/15",
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}
] |
|
Retraining and reskilling workers in the age of automation | McKinsey
|
Retraining and reskilling workers in the age of automation
|
https://www.mckinsey.com
|
[
"Pablo Illanes",
"Susan Lund",
"Mona Mourshed",
"Scott Rutherford",
"Magnus Tyreman"
] |
Executives increasingly see investing in retraining and “upskilling” existing workers as an urgent business priority that companies, not governments, must lead ...
|
The world of work faces an epochal transition. By 2030, according to the a recent McKinsey Global Institute report, Jobs lost, jobs gained: Workforce transitions in a time of automation, as many as 375 million workers—or roughly 14 percent of the global workforce—may need to switch occupational categories as digitization, automation, and advances in artificial intelligence disrupt the world of work. The kinds of skills companies require will shift, with profound implications for the career paths individuals will need to pursue.
Stay current on your favorite topics Subscribe
How big is that challenge? In terms of magnitude, it’s akin to coping with the large-scale shift from agricultural work to manufacturing that occurred in the early 20th century in North America and Europe, and more recently in China. But in terms of who must find new jobs, we are moving into uncharted territory. Those earlier workforce transformations took place over many decades, allowing older workers to retire and new entrants to the workforce to transition to the growing industries. But the speed of change today is potentially faster. The task confronting every economy, particularly advanced economies, will likely be to retrain and redeploy tens of millions of midcareer, middle-age workers. As the MGI report notes, “there are few precedents in which societies have successfully retrained such large numbers of people.”
So far, growing awareness of the scale of the task ahead has yet to translate into action. Indeed, public spending on labor-force training and support has fallen steadily for years in most member countries of the Organisation for Economic Co-Operation and Development (OECD). Nor do corporate-training budgets appear to be on any kind of upswing. But that may be about to change.
Among companies on the front lines, according to a recent McKinsey survey, executives increasingly see investing in retraining and “upskilling” existing workers as an urgent business priority—and they also believe that this is an issue where corporations, not governments, must take the lead. Our survey, which was in the field in late 2017, polled more than 1,500 respondents from business, the public sector, and not for profits across regions, industries, and sectors. The analysis that follows focuses on answers from roughly 300 executives at companies with more than $100 million in annual revenues.
Among this group, 66 percent see “addressing potential skills gaps related to automation/digitization” within their workforces as at least a “top-ten priority.” Nearly 30 percent put it in the top five (Exhibit 1). The driver behind this sense of urgency is the accelerating pace of enterprise-wide transformation. Looking back over the past five years, only about a third of executives in our survey said technological change had caused them to retrain or replace more than a quarter of their employees. But when they look out over the next five years, that narrative changes.
Sixty-two percent of executives believe they will need to retrain or replace more than a quarter of their workforce between now and 2023 due to advancing automation and digitization. The threat looms larger in the United States and Europe (64 percent and 70 percent respectively) than in the rest of the world (only 55 percent)—and it is felt especially acutely among the biggest companies. Seventy percent of executives at companies with more than $500 million in annual revenues see technological disruption over the next five years affecting more than a quarter of their workers.
Would you like to learn more about the McKinsey Global Institute
Appropriately, this keen sense of the challenge ahead comes with a strong feeling of ownership. While they clearly do not expect to solve this alone—forging creative partnerships with a wide range of relevant players, for example, will be critical—by a nearly a 5:1 margin, the executives in our latest survey believe that corporations, not governments, educators, or individual workers, should take the lead in trying to close the looming skills gap. That’s the view of 64 percent of the private-sector executives in the United States who see this as a top-ten priority issue, and 59 percent in Europe (Exhibit 2).
As for solutions, 82 percent of executives at companies with more than $100 million in annual revenues believe retraining and reskilling must be at least half of the answer to addressing their skills gap. Within that consensus, though, were clear regional differences. Fully 94 percent of those surveyed in Europe insisted the answer would either be an equal mix of hiring and retraining or mainly retraining versus a strong but less resounding 62 percent in this camp in the United States. By contrast, 35 percent of Americans thought the challenge would have to be met mainly or exclusively by hiring new talent, compared to just 7 percent in this camp in Europe (Exhibit 3).
Now the bad news: only 16 percent of private-sector business leaders in this group feel “very prepared” to address potential skills gaps, with roughly twice as many feeling either “somewhat unprepared” or “very unprepared.” The majority felt “somewhat prepared”—hardly a clarion call of confidence.
What are the main barriers? About one-third of executives feel an urgent need to rethink and upgrade their current HR infrastructure. Many companies are also struggling to figure out how job roles will change and what kind of talent they will require over the next five to ten years. Some executives who saw this as a top priority—42 percent in the United States, 24 percent in Europe, and 31 percent in the rest of the world—admit they currently lack a “good understanding of how automation and/or digitization will affect our future skills needs.”
Such a high degree of anxiety is understandable. In our experience, too much traditional training and retraining goes off the rails because it delivers no clear pathway to new work, relies too heavily on theory versus practice, and fails to show a return on investment. Generation, a global youth employment not for profit founded in 2015 by McKinsey, deliberately set out to address those shortcomings. Operating in five countries across over 20 professions, Generation operates programs that focus on targeting training to where strong demand for jobs exists and gathers the data needed to prove the return on investment (ROI) to learners and employers. As a result, Generation’s more than 16,000 graduates have over 82 percent job placement, 72 percent job retention at one year, and two to six times higher income than prior to the program. Generation will soon pilot a new initiative, Re-Generation, to apply this same formula—which includes robust partnerships with employers, governments and not for profits—to helping mid-career employees learn new skills for new jobs.
For many companies, cracking the code on reskilling is partly about retaining their “license to operate” by empowering employees to be more productive. Thirty-eight percent of executives in our survey, across all regions, cited the desire to “align with our organization’s mission and values” as a key reason for taking action. In a similar vein, at last winter’s World Economic Forum in Davos, 80 percent of CEOs who were investing heavily in artificial intelligence also publicly pledged to retain and retrain existing employees.
But the biggest driver is this: as digitization, automation, and AI reshape whole industries and every enterprise, the only way to realize the potential productivity dividends from that investment will be to have the people and processes in place to capture it. Managing this transition well, in short, is not just a social good; it’s a competitive imperative. That’s why a resounding majority of respondents—64 percent across Europe, the United States, and the rest of the world—said the main reason they were willing to invest in retraining was “to increase employee productivity.”
We hear that thought echoed in a growing number of C-suite conversations we are having these days. At the moment, most top executives have far more questions than answers about what it will take to meet the reskilling challenge at the kind of scale the next decade will likely demand. They ask: How can I map the future against my current talent pool and processes? What part of future employment demand can I meet by retraining existing workers, and what is the ROI of doing so, versus simply hiring new ones? How best can I tap into what are, for me, nontraditional talent pools? What partners, either in the private, public, or nongovernmental-organization (NGO) sectors, might help me succeed—and what are our respective roles?
What the future of work will mean for jobs, skills, and wages
Good questions all. Over the coming months we intend to share more of our own thinking and analytical work—and some of the best ideas we are finding elsewhere—about the solutions that are emerging. Success will require first developing a granular map of how technology will change the skill requirements within your company. Once this is understood, the next step will be deciding whether to tap into new models of online and offline learning and training or partner with traditional educational providers. (Over time, a more fundamental rethinking of 100-year-old educational models will also be needed.) Policy makers will need to consider new forms of unemployment income and worker transition support, and foster more intensive and innovative collaboration between the public and private sectors. Individuals will need to step up too, as will governments. Depending on the speed and scale of the coming workforce transition, as MGI noted in its recent report, many countries may conclude they will need to undertake “initiatives on the scale of the Marshall plan.”
But for now, we simply take comfort from the clear message of our latest survey: among large companies, senior executives see an urgent need to rethink and retool their role in helping workers develop the right skills for a rapidly changing economy—and their will to meet this challenge is strong. That’s not a bad place to start.
| 2018-01-22T00:00:00 |
https://www.mckinsey.com/featured-insights/future-of-work/retraining-and-reskilling-workers-in-the-age-of-automation
|
[
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|
The Question with AI Isn't Whether We'll Lose Our Jobs
|
The Question with AI Isn’t Whether We’ll Lose Our Jobs — It’s How Much We’ll Get Paid
|
https://hbr.org
|
[
"Lori G. Kletzer",
"Is A Professor Of Economics At Colcollege"
] |
With robotics, artificial intelligence, and machine learning, what we call automation seems poised to take on a greater share of high- ...
|
The Question with AI Isn’t Whether We’ll Lose Our Jobs — It’s How Much We’ll Get Paid
The basic fact is that technology eliminates jobs, not work. It is the continuous obligation of economic policy to match increases in productive potential with increases in purchasing power and demand. Otherwise the potential created by technical progress runs to waste in idle capacity, unemployment, and deprivation. —National Commission on Technology, Automation and Economic Progress, Technology and the American Economy, Volume 1, February 1966, pg. 9.
| 2018-01-31T00:00:00 |
2018/01/31
|
https://hbr.org/2018/01/the-question-with-ai-isnt-whether-well-lose-our-jobs-its-how-much-well-get-paid
|
[
{
"date": "2018/01/31",
"position": 95,
"query": "robotics job displacement"
}
] |
Reskilling Our Workforce for the Age of Automation
|
Reskilling Our Workforce for the Age of Automation
|
https://www.roboticstomorrow.com
|
[] |
Advanced technologies such as AI and machine learning haven't been integrated into higher education quickly enough, and it's creating a skills ...
|
Technology is rapidly outpacing many traditional educational institutions, as they are prone to being slowed by bureaucracy and arent agile enough to embrace the necessary changes needed to provide employable graduates to todays workforce. Reskilling Our Workforce for the Age of Automation P.K. Agarwal | Northeastern University: Silicon Valley
What exactly are you seeing as the “skills gap” in our workforce when it comes to AI and machine automation?
In today’s rapidly changing tech sector, many students are finding it challenging to keep up with the times. Advanced technologies such as AI and machine learning haven’t been integrated into higher education quickly enough, and it’s creating a skills gap for graduates who are not qualified to meet critical market demands. Recent research from Element AI showed that only 90,000 people globally have the right skills for today’s artificial intelligence/machine learning needs (if they don’t need to have a PhD awarded since 2015). Due to this lack of skilled talent, tech giants are offering VERY competitive salaries in this field, making it an area with huge opportunities both in education and for professionals in the field. Northeastern University appreciates this challenge (and opportunity) and is therefore moving quickly to integrate these topics into our programs.
How do you see machine automation and AI changing the way we need to educate the workforce?
Regardless of their academic inclinations, all students will require a new kind of literacy for the 21st century which includes quantitative “hard” skills blended with humanities “soft” skills and high levels of creativity. We will need to prepare students for new types of jobs as automation continues to rapidly eliminate traditional roles. “Humanics”—a blend of technical and social skills—is the key to creating a well-rounded, agile workforce.
How can we robot-proof a professional to ensure that they won’t lose their jobs to automation?
Jobs requiring manual, repetitive tasks without human interaction are primed to be replaced by robots or automation, but the need for relational soft skills like negotiation, conflict resolution and working between and among complex networks of a corporate hierarchy will most likely always require a human (and humane) touch. Professionals might also need to “re-skill” in order to stay relevant in the automated workplace. This is where programs such as Northeastern University’s ALIGN —obtaining a computer science master’s degree without having to have a computer science background—becomes highly useful.
Where are the best opportunities for highly skilled professionals today? Tomorrow?
STEM will remain a lucrative area for the next decade or two – at a minimum. Within STEM fields, tech, biotech, and specialized topics like IoT, Big data, AI, machine learning, cybersecurity are projected to continue facing significant gap in supply versus demand. Functionally, due to the aging Baby Boomer population in the US, healthcare is experiencing expanding opportunities. New roles in healthcare are being created rapidly to meet these needs as well as newly introduced roles focused on integrated healthcare technologies, IT in healthcare and beyond.
As we see more menial tasks being taken over by automation, more opportunities in highly cognitive jobs are arising. We continue to see a shortage of skilled STEM workers, teachers, health services providers and jobs with high social skills levels like therapists and social services managers. This shift means that jobs will require more skills like complex problem solving, critical thinking and creativity more than specific technical know-how. By focusing education on these skills, we can best equip ourselves for the jobs of .
What do you think the most important skills are for our workforce today?
As advanced as a robot can be, humans will always prevail when it comes to soft skills like working in a team, emotional intelligence, and the various forms of communication and the methods of achieving team goals. Programs that cater and appeal to those with a non-traditional STEM background are undoubtedly part of the solution. One way to approach this is to reskill those with a non-STEM bachelor’s degree to secure advanced degrees in computer science. These types of program should offer flexibility in scheduling to accommodate young parents or those working full-time. Making certain to design these programs with an inclusionary spirit will help bring more diversity to all technical fields.
What training programs are the most popular with professionals looking to “get ahead” and learn a new skill set?
Today’s workers must have a thirst for life-long learning. We no longer live in a society that can sustain a 20-year long career in a single discipline. Shorter programs to get the skills needed quickly are highly popular with professionals looking to get a competitive edge.
Are we using AI to in fact design and implement training programs for these skills?
Early implementations in training have had machines customizing learning based on individual needs. However, the social and human aspect of learning will still play a dominant role. We are seeing more and more language software learning tools that are customizing curriculum on the fly for the student, providing a more tailored experience. We should take care not lean too heavily on software for learning, it is important not to remove the human element from training since growing evidence shows that social aspects of learning are critical.
Do you think our educational system is preparing the next generation of graduates for this new reality?
Many universities are working quickly to adapt their curriculum to market these skills, but some are moving faster than others. Technology is rapidly outpacing many traditional educational institutions, as they are prone to being slowed by bureaucracy and aren’t agile enough to embrace the necessary changes needed to provide employable graduates to today’s workforce. At Northeastern University, we answer this challenge by providing up-to-date, highly relevant skills in our programs in order to “robot-proof” our students. We also work hard to ensure our learning is 100 percent experiential. Our graduates have the opportunity to learn from real work environments during their studies rather than just focusing on theory. This experiential learning gives students the in-demand skills they need like problem solving and critical thinking to help them succeed in any industry. As the job landscape changes, we want to provide our students with transferable skills to help protect them from changes due to automation and machine learning, we want them to be have “robot-proof” skills that will serve them for a lifetime.
What occupations are likely to be most impacted due to AI/Machine Learning?
Repetitive tasks and predictable processes are the most likely to disappear first. Secretaries, typists and routine roles will continue to become less common. Even jobs like picking fruit and making coffee are increasingly becoming replaced by machines, however, there are also new jobs being created that focus on cognitive skills. IoT officers are relatively new to the workforce, and new roles are being created every day. Right now jobs in renewable energy, healthcare and data evaluation are growing, and should continue to do so for the foreseeable future. We should take care not to view the elimination of jobs as grim, but rather as an opportunity to reshape our workforce and allow greater work/life balance in the future. Just as many technological advances in history have shown us, we should look to what we can gain in new roles and opportunities to see that there should be a net-positive result from new innovation.
About P.K. Agarwal
P.K. Agarwal serves as the Dean and CEO of Northeastern University–Silicon Valley. He also serves as the Chairman of Future 500, a Bay Area-based pioneer in the area of global sustainability. Formerly, he was the CEO of TiE Global, an organization dedicated to fostering entrepreneurship across 61 cities in 18 countries. Prior to TiE, P.K served as Governor Arnold Schwarzenegger’s Chief Technology Officer for the State of California. He has also been in executive and management roles with ACS (now Xerox), NIC Inc., and EDS (now HP). PK helped pioneer the use of Internet in government and shaped the national and state policy in this area, dating back to Al Gore’s National Information Infrastructure Advisory Council in 1995. He had the unique distinction of having a U.S. national annual award named as the “P.K. Agarwal Award for Leadership in Electronic Government.” He also served as the president of the National Association of State CIOs and the National Electronic Commerce Coordinating Council (ec3). He is a fellow of the National Academy of Public Administration and an adjunct faculty at USC and USF.
The content & opinions in this article are the author’s and do not necessarily represent the views of RoboticsTomorrow
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| 2018-03-06T00:00:00 |
https://www.roboticstomorrow.com/article/2018/02/reskilling-our-workforce-for-the-age-of-automation/11455
|
[
{
"date": "2018/03/06",
"position": 80,
"query": "reskilling AI automation"
},
{
"date": "2018/03/06",
"position": 69,
"query": "reskilling AI automation"
}
] |
|
Economist predicts job loss to machines, but sees long-term hope
|
Economist predicts job loss to machines, but sees long-term hope
|
https://phys.org
|
[
"Rich Barlow",
"Boston University"
] |
BU economist Pascual Restrepo says that interpretation is too gloomy, although his recent research, posted online by the National Bureau of ...
|
Credit: Boston University
Are we bumping up against the "Robocalypse," when automation sweeps industry and replaces human workers with machines? BU economist Pascual Restrepo says that interpretation is too gloomy, although his recent research, posted online by the National Bureau of Economic Research, reveals that the adoption of just one industrial robot eliminates nearly six jobs in a community.
The study, examining job losses between 1990 and 2007, modified earlier research by Restrepo, a College of Arts & Sciences assistant professor of economics, and coauthor Daron Acemoglu of MIT. That previous work, says Restrepo, was a "conceptual exercise" that looked at history and argued that over the long run, automation transforms rather than eliminates human jobs.
He still believes that. But he realized that regaining jobs is a longer slog after he and Acemoglu looked at actual data from 19 industries—among them car manufacturing, electronics, pharmaceuticals, plastics, chemicals, and food processing—that introduced industrial robots. Those are multipurpose, reprogrammable machines, as opposed to artificial intelligence technology and single-purpose machines (coffee machines qualify, as one mundane-minded analyst remarked).
That real-world data showed the one-for-six robot-for-humans swap in communities most exposed to automation in industry.
Those job losses and the potential for others (software has been developed that can take over middle-management work) has some Silicon Valley wonks predicting a workless future where Americans will need a government-provided income to replace wages.
But Restrepo says interpretations of his research as foretelling the demise of human work are premature. "The process of machines replacing human labor is not something that is new," he says. "It's been going on for 200 years. Why is it the case that we still have so many jobs?
"We went, for instance, from having like 60 percent of the population working in agriculture to having 3 percent working in agriculture," but that led to the rise of industrial jobs, he says. And as manufacturing petered out, workers shifted into the service sector: "Who would have imagined 30 years ago that we would have people designing apps, working as software engineers?…Who knows what our kids are going to be doing 30 years from now?"
Still, the transition to jobs of the future is "actually quite painful," as workers automated out of their jobs don't have an easy time migrating into new employment.
"Communities that have been more exposed to automation," Restrepo says, "do not tend to be doing well in terms of employment and wages." He and Acemoglu found that many workers dropped out of the workforce and "just stopped looking for a job, because they got discouraged.
"These places do not seem to be developing new jobs or new industries to absorb these workers, and that's our concern," Restrepo says. "That's not saying that it's not going to happen, maybe in 10 years…but the thing is that so far we're not seeing it." That's noteworthy he adds, because the study included the booming 1990s.
There has often been a decades-long lag between past waves of automation and workers moving to newly created jobs with good wages, he says. "These adjustments were never easy. There was a lot of turmoil in between, there was a lot of political unrest in between."
Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights. Sign up for our free newsletter and get updates on breakthroughs, innovations, and research that matter—daily or weekly.
Opposition to automation won't fix the problem. "At the end of the day, technology is the reason why we have such a high standard of living," he says. While the duo's study doesn't address solutions to workers' plight, Restrepo says humans can choose to use tech for more than we are using it for currently.
"It seems to me that we're emphasizing the use of technology to automate existing uses of labor," replacing blue-collar and clerical workers. "We can also use technology to augment workers, to create new types of jobs" to soak up the workers who've dropped out of job-seeking.
Another prudent choice, he says, would be improving government assistance to workers, such as expanding the earned income tax credit, which gives subsidies to the working poor, to help those individuals who've stopped working because the available jobs don't pay as well as their old ones. Better retraining programs, and college-business partnerships helping colleges prepare workers for new jobs with businesses, also would be a good idea, he argues.
"Opinion is moving between sort of polar extremes. On the one extreme is people that are claiming the Robocalpyse is coming," he says. On the other hand, "many economists view this like, oh, we've been here before" and new jobs will come.
Since publication of his study, Restrepo has turned his focus to Germany and Japan, leaders in adopting industrial robots. But they need to do that because their workforces are aging and they face a labor shortage, so automation is a reasonable choice, he says.
More information: Daron Acemoglu et al. Robots and Jobs: Evidence from US Labor Markets, (2017). DOI: 10.3386/w23285
| 2018-03-19T00:00:00 |
https://phys.org/news/2018-03-economist-job-loss-machines-long-term.html
|
[
{
"date": "2018/03/19",
"position": 80,
"query": "robotics job displacement"
},
{
"date": "2018/03/19",
"position": 81,
"query": "robotics job displacement"
},
{
"date": "2018/03/19",
"position": 75,
"query": "robotics job displacement"
},
{
"date": "2018/03/19",
"position": 81,
"query": "robotics job displacement"
}
] |
|
AI and Workforce Transformation: What It May Mean to Education ...
|
AI and Workforce Transformation: What It May Mean to Education, Employee Skills and Operations
|
https://www.niceactimize.com
|
[
"July",
"June",
"May",
"April",
"February"
] |
The recent shift from knowledge worker to that of advisor has changed our workforce in demonstrable ways, requiring new approaches to staffing and workforce ...
|
The recent shift from knowledge worker to that of advisor has changed our workforce in demonstrable ways, requiring new approaches to staffing and workforce management structures. Like many organizations in the financial services industry, we at NICE Actimize are enthusiastic about the potential that the new wave of artificial intelligence-based technologies brings to our workforce. But, we are also seeing that this shift requires not just new ways of managing our operations, but with it new ways of managing how we view our skills inventories from a human perspective.
Despite this wave of change, and our industry’s focus on robotic process automation and AI, we still know and shouldn’t ever forget that human involvement is still paramount and behind every action and decision-making process with respect to financial crime, risk and compliance operations.
Recently, I had an interesting opportunity to discuss what this wave of transformation might mean in terms of workforce education and management – the Waters reporter interviewing me on this topic posed that perhaps we need to rethink how we are shaping our talent and how we are preparing new graduates to face this new world of automation. And, she explored, despite the impressive skill sets of our current crop of tech graduates emerging from some of our finest graduate schools in computer sciences, analytics, and even robotics, perhaps these newcomers to the workforce may be lacking certain competencies in the humanities that would make them better aligned to manage a future filled with robotics and artificial intelligence – such as ethics courses or behavioral sciences.
Interesting concepts that even Arthur C. Clarke would have enjoyed debating with the creation of the sentient HAL computer. Are we prepared for that inevitable future?
Moving back to the present, what I told Waters in our interview was, “There has been a shift in the workforce that started from automation, and we began to see dramatic benefits and an impact on the kind of jobs available — a shift from administrative, to knowledge workers, to a more advisory position. Soon, we will see analysts replaced by computers and we will no longer need analytics done by humans, but more mimicry of humans. So, we will need people to advise these machines on how to work. We need not just an understanding of how computers work, but how people interact. We need to combine all skill sets. We need to expose ourselves to a broader set of ideas and experiences.”
Technology continues to advance both our lives and our workforce – and they say that the rate of change over the next three years will be three times faster than the prior three years, dizzying to say the least. With this, comes additional breakthroughs in deep learning, and more access to an increasing amount of data. This paradigm shift will clearly change what all businesses – including banking and financial services – will be looking for in the “new” workforce of tomorrow.
With great anticipation, we look forward to the progress and continued advancements that this new thinking will bring to our businesses and its certain to be positive impact on workforce development. Is your organization ready to embrace the change?
| 2018-05-07T00:00:00 |
2018/05/07
|
https://www.niceactimize.com/blog/ai-and-workforce-transformation-what-it-may-mean-to-education-employee-skills-and-operations-575/
|
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AI, automation, and the future of work: Ten things to solve for
|
AI, automation, and the future of work: Ten things to solve for
|
https://www.mckinsey.com
|
[
"James Manyika",
"Kevin Sneader"
] |
How AI and automation will affect work · About half of the activities (not jobs) carried out by workers could be automated · Would you like to ...
|
Automation and artificial intelligence (AI) are transforming businesses and will contribute to economic growth via contributions to productivity. They will also help address “moonshot” societal challenges in areas from health to climate change.
Stay current on your favorite topics Subscribe
At the same time, these technologies will transform the nature of work and the workplace itself. Machines will be able to carry out more of the tasks done by humans, complement the work that humans do, and even perform some tasks that go beyond what humans can do. As a result, some occupations will decline, others will grow, and many more will change.
While we believe there will be enough work to go around (barring extreme scenarios), society will need to grapple with significant workforce transitions and dislocation. Workers will need to acquire new skills and adapt to the increasingly capable machines alongside them in the workplace. They may have to move from declining occupations to growing and, in some cases, new occupations.
This executive briefing, which draws on the latest research from the McKinsey Global Institute, examines both the promise and the challenge of automation and AI in the workplace and outlines some of the critical issues that policy makers, companies, and individuals will need to solve for.
Accelerating progress in AI and automation is creating opportunities for businesses, the economy, and society
Automation and AI are not new, but recent technological progress is pushing the frontier of what machines can do. Our research suggests that society needs these improvements to provide value for businesses, contribute to economic growth, and make once unimaginable progress on some of our most difficult societal challenges. In summary:
Rapid technological progress
Beyond traditional industrial automation and advanced robots, new generations of more capable autonomous systems are appearing in environments ranging from autonomous vehicles on roads to automated check-outs in grocery stores. Much of this progress has been driven by improvements in systems and components, including mechanics, sensors and software. AI has made especially large strides in recent years, as machine-learning algorithms have become more sophisticated and made use of huge increases in computing power and of the exponential growth in data available to train them. Spectacular breakthroughs are making headlines, many involving beyond-human capabilities in computer vision, natural language processing, and complex games such as Go.
Potential to transform businesses and contribute to economic growth
These technologies are already generating value in various products and services, and companies across sectors use them in an array of processes to personalize product recommendations, find anomalies in production, identify fraudulent transactions, and more. The latest generation of AI advances, including techniques that address classification, estimation, and clustering problems, promises significantly more value still. An analysis we conducted of several hundred AI use cases found that the most advanced deep learning techniques deploying artificial neural networks could account for as much as $3.5 trillion to $5.8 trillion in annual value, or 40 percent of the value created by all analytics techniques (Exhibit 1).
Deployment of AI and automation technologies can do much to lift the global economy and increase global prosperity, at a time when aging and falling birth rates are acting as a drag on growth. Labor productivity growth, a key driver of economic growth, has slowed in many economies, dropping to an average of 0.5 percent in 2010–2014 from 2.4 percent a decade earlier in the United States and major European economies, in the aftermath of the 2008 financial crisis after a previous productivity boom had waned. AI and automation have the potential to reverse that decline: productivity growth could potentially reach 2 percent annually over the next decade, with 60 percent of this increase from digital opportunities.
Potential to help tackle several societal moonshot challenges
AI is also being used in areas ranging from material science to medical research and climate science. Application of the technologies in these and other disciplines could help tackle societal moonshot challenges. For example, researchers at Geisinger have developed an algorithm that could reduce diagnostic times for intracranial hemorrhaging by up to 96 percent. Researchers at George Washington University, meanwhile, are using machine learning to more accurately weight the climate models used by the Intergovernmental Panel on Climate Change.
Challenges remain before these technologies can live up to their potential for the good of the economy and society everywhere
AI and automation still face challenges. The limitations are partly technical, such as the need for massive training data and difficulties “generalizing” algorithms across use cases. Recent innovations are just starting to address these issues. Other challenges are in the use of AI techniques. For example, explaining decisions made by machine learning algorithms is technically challenging, which particularly matters for use cases involving financial lending or legal applications. Potential bias in the training data and algorithms, as well as data privacy, malicious use, and security are all issues that must be addressed. Europe is leading with the new General Data Protection Regulation, which codifies more rights for users over data collection and usage.
A different sort of challenge concerns the ability of organizations to adopt these technologies, where people, data availability, technology, and process readiness often make it difficult. Adoption is already uneven across sectors and countries. The finance, automotive, and telecommunications sectors lead AI adoption. Among countries, US investment in AI ranked first at $15 billion to $23 billion in 2016, followed by Asia’s investments of $8 billion to $12 billion, with Europe lagging behind at $3 billion to $4 billion.
Section 2
How AI and automation will affect work
Even as AI and automation bring benefits to business and society, we will need to prepare for major disruptions to work.
About half of the activities (not jobs) carried out by workers could be automated
Our analysis of more than 2000 work activities across more than 800 occupations shows that certain categories of activities are more easily automatable than others. They include physical activities in highly predictable and structured environments, as well as data collection and data processing. These account for roughly half of the activities that people do across all sectors. The least susceptible categories include managing others, providing expertise, and interfacing with stakeholders.
Nearly all occupations will be affected by automation, but only about 5 percent of occupations could be fully automated by currently demonstrated technologies. Many more occupations have portions of their constituent activities that are automatable: we find that about 30 percent of the activities in 60 percent of all occupations could be automated. This means that most workers—from welders to mortgage brokers to CEOs—will work alongside rapidly evolving machines. The nature of these occupations will likely change as a result.
Would you like to learn more about the McKinsey Global Institute
Jobs lost: Some occupations will see significant declines by 2030
Automation will displace some workers. We have found that around 15 percent of the global workforce, or about 400 million workers, could be displaced by automation in the period 2016–2030. This reflects our midpoint scenario in projecting the pace and scope of adoption. Under the fastest scenario we have modeled, that figure rises to 30 percent, or 800 million workers. In our slowest adoption scenario, only about 10 million people would be displaced, close to zero percent of the global workforce (Exhibit 2).
The wide range underscores the multiple factors that will impact the pace and scope of AI and automation adoption. Technical feasibility of automation is only the first influencing factor. Other factors include the cost of deployment; labor-market dynamics, including labor-supply quantity, quality, and the associated wages; the benefits beyond labor substitution that contribute to business cases for adoption; and, finally, social norms and acceptance. Adoption will continue to vary significantly across countries and sectors because of differences in the above factors, especially labor-market dynamics: in advanced economies with relatively high wage levels, such as France, Japan, and the United States, automation could displace 20 to 25 percent of the workforce by 2030, in a midpoint adoption scenario, more than double the rate in India.
Jobs gained: In the same period, jobs will also be created
Even as workers are displaced, there will be growth in demand for work and consequently jobs. We developed scenarios for labor demand to 2030 from several catalysts of demand for work, including rising incomes, increased spending on healthcare, and continuing or stepped-up investment in infrastructure, energy, and technology development and deployment. These scenarios showed a range of additional labor demand of between 21 percent to 33 percent of the global workforce (555 million and 890 million jobs) to 2030, more than offsetting the numbers of jobs lost. Some of the largest gains will be in emerging economies such as India, where the working-age population is already growing rapidly.
Additional economic growth, including from business dynamism and rising productivity growth, will also continue to create jobs. Many other new occupations that we cannot currently imagine will also emerge and may account for as much as 10 percent of jobs created by 2030, if history is a guide. Moreover, technology itself has historically been a net job creator. For example, the introduction of the personal computer in the 1970s and 1980s created millions of jobs not just for semiconductor makers, but also for software and app developers of all types, customer-service representatives, and information analysts.
Jobs changed: More jobs than those lost or gained will be changed as machines complement human labor in the workplace
Partial automation will become more prevalent as machines complement human labor. For example, AI algorithms that can read diagnostic scans with a high degree of accuracy will help doctors diagnose patient cases and identify suitable treatment. In other fields, jobs with repetitive tasks could shift toward a model of managing and troubleshooting automated systems. At retailer Amazon, employees who previously lifted and stacked objects are becoming robot operators, monitoring the automated arms and resolving issues such as an interruption in the flow of objects.
Section 3
Key workforce transitions and challenges
While we expect there will be enough work to ensure full employment in 2030 based on most of our scenarios, the transitions that will accompany automation and AI adoption will be significant. The mix of occupations will change, as will skill and educational requirements. Work will need to be redesigned to ensure that humans work alongside machines most effectively.
Workers will need different skills to thrive in the workplace of the future
Automation will accelerate the shift in required workforce skills we have seen over the past 15 years. Demand for advanced technological skills such as programming will grow rapidly. Social, emotional, and higher cognitive skills, such as creativity, critical thinking, and complex information processing, will also see growing demand. Basic digital skills demand has been increasing and that trend will continue and accelerate. Demand for physical and manual skills will decline but will remain the single largest category of workforce skills in 2030 in many countries (Exhibit 3). This will put additional pressure on the already existing workforce-skills challenge, as well as the need for new credentialing systems. While some innovative solutions are emerging, solutions that can match the scale of the challenge will be needed.
Many workers will likely need to change occupations
Our research suggests that, in a midpoint scenario, around 3 percent of the global workforce will need to change occupational categories by 2030, though scenarios range from about 0 to 14 percent. Some of these shifts will happen within companies and sectors, but many will occur across sectors and even geographies. Occupations made up of physical activities in highly structured environments or in data processing or collection will see declines. Growing occupations will include those with difficult to automate activities such as managers, and those in unpredictable physical environments such as plumbers. Other occupations that will see increasing demand for work include teachers, nursing aides, and tech and other professionals.
Workplaces and workflows will change as more people work alongside machines
As intelligent machines and software are integrated more deeply into the workplace, workflows and workspaces will continue to evolve to enable humans and machines to work together. As self-checkout machines are introduced in stores, for example, cashiers can become checkout assistance helpers, who can help answer questions or troubleshoot the machines. More system-level solutions will prompt rethinking of the entire workflow and workspace. Warehouse design may change significantly as some portions are designed to accommodate primarily robots and others to facilitate safe human-machine interaction.
Skill shift: Automation and the future of the workforce
Automation will likely put pressure on average wages in advanced economies
The occupational mix shifts will likely put pressure on wages. Many of the current middle-wage jobs in advanced economies are dominated by highly automatable activities, such as in manufacturing or in accounting, which are likely to decline. High-wage jobs will grow significantly, especially for high-skill medical and tech or other professionals, but a large portion of jobs expected to be created, including teachers and nursing aides, typically have lower wage structures. The risk is that automation could exacerbate wage polarization, income inequality, and the lack of income advancement that has characterized the past decade across advanced economies, stoking social, and political tensions.
In the face of these looming challenges, workforce challenges already exist
Most countries already face the challenge of adequately educating and training their workforces to meet the current requirements of employers. Across the OECD, spending on worker education and training has been declining over the last two decades. Spending on worker transition and dislocation assistance has also continued to shrink as a percentage of GDP. One lesson of the past decade is that while globalization may have benefited economic growth and people as consumers, the wage and dislocation effects on workers were not adequately addressed. Most analyses, including our own, suggest that the scale of these issues is likely to grow in the coming decades. We have also seen in the past that large-scale workforce transitions can have a lasting effect on wages; during the 19th century Industrial Revolution, wages in the United Kingdom remained stagnant for about half a century despite rising productivity—a phenomenon known as “Engels’ Pause,” (PDF–690KB) after the German philosopher who identified it.
Section 4
Ten things to solve for
In the search for appropriate measures and policies to address these challenges, we should not seek to roll back or slow diffusion of the technologies. Companies and governments should harness automation and AI to benefit from the enhanced performance and productivity contributions as well as the societal benefits. These technologies will create the economic surpluses that will help societies manage workforce transitions. Rather, the focus should be on ways to ensure that the workforce transitions are as smooth as possible. This is likely to require actionable and scalable solutions in several key areas:
Ensuring robust economic and productivity growth. Strong growth is not the magic answer for all the challenges posed by automation, but it is a prerequisite for job growth and increasing prosperity. Productivity growth is a key contributor to economic growth. Therefore, unlocking investment and demand, as well as embracing automation for its productivity contributions, is critical.
Fostering business dynamism. Entrepreneurship and more rapid new business formation will not only boost productivity, but also drive job creation. A vibrant environment for small businesses as well as a competitive environment for large business fosters business dynamism and, with it, job growth. Accelerating the rate of new business formation and the growth and competitiveness of businesses, large and small, will require simpler and evolved regulations, tax and other incentives.
Evolving education systems and learning for a changed workplace. Policy makers working with education providers (traditional and nontraditional) and employers themselves could do more to improve basic STEM skills through the school systems and improved on-the-job training. A new emphasis is needed on creativity, critical and systems thinking, and adaptive and life-long learning. There will need to be solutions at scale.
Investing in human capital. Reversing the trend of low, and in some countries, declining public investment in worker training is critical. Through tax benefits and other incentives, policy makers can encourage companies to invest in human capital, including job creation, learning and capability building, and wage growth, similar to incentives for private sector to invest in other types of capital including R&D.
Improving labor-market dynamism. Information signals that enable matching of workers to work, credentialing, could all work better in most economies. Digital platforms can also help match people with jobs and restore vibrancy to the labor market. When more people change jobs, even within a company, evidence suggests that wages rise. As more varieties of work and income-earning opportunities emerge including the gig economy, we will need to solve for issues such as portability of benefits, worker classification, and wage variability.
Redesigning work. Workflow design and workspace design will need to adapt to a new era in which people work more closely with machines. This is both an opportunity and a challenge, in terms of creating a safe and productive environment. Organizations are changing too, as work becomes more collaborative and companies seek to become increasingly agile and nonhierarchical.
Rethinking incomes. If automation (full or partial) does result in a significant reduction in employment and/or greater pressure on wages, some ideas such as conditional transfers, support for mobility, universal basic income, and adapted social safety nets could be considered and tested. The key will be to find solutions that are economically viable and incorporate the multiple roles that work plays for workers, including providing not only income, but also meaning, purpose, and dignity.
Rethinking transition support and safety nets for workers affected. As work evolves at higher rates of change between sectors, locations, activities, and skill requirements, many workers will need assistance adjusting. Many best practice approaches to transition safety nets are available, and should be adopted and adapted, while new approaches should be considered and tested.
Investing in drivers of demand for work. Governments will need to consider stepping up investments that are beneficial in their own right and will also contribute to demand for work (for example, infrastructure, climate-change adaptation). These types of jobs, from construction to rewiring buildings and installing solar panels, are often middle-wage jobs, those most affected by automation.
Embracing AI and automation safely. Even as we capture the productivity benefits of these rapidly evolving technologies, we need to actively guard against the risks and mitigate any dangers. The use of data must always take into account concerns including data security, privacy, malicious use, and potential issues of bias, issues that policy makers, tech and other firms, and individuals will need to find effective ways to address.
There is work for everyone today and there will be work for everyone tomorrow, even in a future with automation. Yet that work will be different, requiring new skills, and a far greater adaptability of the workforce than we have seen. Training and retraining both midcareer workers and new generations for the coming challenges will be an imperative. Government, private-sector leaders, and innovators all need to work together to better coordinate public and private initiatives, including creating the right incentives to invest more in human capital. The future with automation and AI will be challenging, but a much richer one if we harness the technologies with aplomb—and mitigate the negative effects.
| 2018-06-01T00:00:00 |
https://www.mckinsey.com/featured-insights/future-of-work/ai-automation-and-the-future-of-work-ten-things-to-solve-for
|
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|
Don't fear the future: how AI can promote job creation - Guild.co
|
Don't fear the future: how AI can promote job creation
|
https://guild.co
|
[
"Written By"
] |
One example of a specific industry where jobs will be created and eliminated is healthcare. The report indicates that while 12% of jobs in ...
|
The artificial intelligence market is expected to be worth $59.75 billion by 2025. If fulfilled, this prediction means ample growth for the market which was valued at less than $1.38 billion in 2016.
Growth in the AI market has positive implications for the technology industry as a whole, heralding an era of job creation and an influx into the global economy.
Much has been made of the impact AI will have on the economy. In particular, many have predicted that AI will lead to the elimination of hundreds of thousands of jobs. But while automation and efficiency achieved through AI will surely lead to changes in the kind of jobs available to the public, this technology is actually predicted to inspire job growth.
Tech companies are already creating jobs
As the numbers of machines and AI devices increases, the need for people to operate and maintain this technology will also increase.
Look at Uber as an example. Uber began as a ride-hailing service, offering thousands of job opportunities to drivers in countries around the globe. When Uber launched its driverless-car program, many feared the program would lead to independent drivers being phased about. While ultimately the need for drivers could be eliminated, this is still a long way off.
Meanwhile, Uber’s driverless car program has actually created thousands more jobs. Certainly Uber has hired coders and other tech experts to build it’s AI-backed programming, but the company has also hired vehicle operators, many of whom have little tech knowledge, to man it’s driverless cars during the testing phase.
This is just one example of how AI is actually creating jobs. Ultimately for decades to come, humans will be needed to run AI, from development and testing, to support, maintenance, and programming.
The days when AI machines will be able to run on their own are a long way off.
Studies say AI won’t destroy more than it creates
While there are other examples like Uber, AI job creation is being studied on a broader level to determine the actual impact it will have and is having on the economy. From this research, experts are predicting that out of all the new jobs created by AI, two thirds will be for people and only one third will be filled by machines.
A new report from PwC echoes the belief that AI will not destroy more jobs than it creates. Researchers at the firm analysed 200,000 jobs in 29 countries to discover the economic benefits and potential challenges posed by automation. Overall, the report found that while 7 million existing jobs are projected to be displaced, an estimated 7.2 million are projected to be created, leading to approximate job growth of 200,000.
In the UK alone, the report predicts that by 2037, 20% of jobs in the country will be displaced by AI, but PwC researchers say the technology will lead to the creation of a nearly equal number of jobs.
One example of a specific industry where jobs will be created and eliminated is healthcare. The report indicates that while 12% of jobs in healthcare will be displaced by AI, 34% will be created.
But that doesn’t mean AI won’t decrease the need for human workers in some sectors. The report found that AI will impact jobs in the transportation and manufacturing industries. According to the research, AI is predicted to displace 38% of transport jobs, and 30% of manufacturing jobs.
Not all of the research currently available is speculative. Firms are also studying companies currently creating jobs with AI.
A CapGemini report - based on data from 1,000 companies - suggests that implementing AI has actually led to job creation in 80% of cases.
A new workforce
The field of AI is still in its infancy, meaning there are still few individuals with the necessary education and knowledge to adequately fulfill jobs in this industry. Estimates about the number of qualified individuals in the field vary.
Technology solutions provider Element AI put the total global population of AI talent at 22,000. But according to the company’s report, only 14% of these individuals are actually looking for work at a given time.
Conversely, Tencent, a Chinese multinational investment holding conglomerate, estimates the number of qualified candidates is between 200,000 and 300,000 people globally.
This makes AI jobs an untapped resource. And experts believe AI job creation could be a boon to developing countries.
Take for example countries in the continent of Africa. According to a report by the World Economic Forum, more than 60% of the sub-Saharan Africa population is under the age of 25. This represents a massive, workforce that if utilized could have a revolutionary impact on the economies of these countries. With the right education programs in place, countries in Africa could prepare their youth to take advantage of the coming jobs and make themselves a hub for AI.
As these examples show, while AI will lead to the elimination of some jobs, it will also see the creation of thousands of jobs in sectors not yet conceived.
In order to ensure this growth has a positive impact on the economy, people must make sure they’re prepared for the jobs of the future.
Photo by Brian Kostiuk on Unsplash
Start using Guild for free or contact us if you want to know more or have questions.
| 2018-08-02T00:00:00 |
2018/08/02
|
https://guild.co/blog/dont-fear-the-future-how-ai-can-promote-job-creation/
|
[
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Is Artificial Intelligence Replacing Jobs? Here's The Truth
|
Is Artificial Intelligence Really Replacing Jobs? Here's The Truth
|
https://www.weforum.org
|
[] |
Our central estimate is that only around 20% of existing UK jobs may actually be displaced by AI and related technologies over the 20 years to 2037.
|
Automation is nothing new – machines have been replacing human workers at a gradual rate ever since the Industrial Revolution. This happened first in agriculture and skilled crafts like hand weaving, then in mass manufacturing and, in more recent decades, in many clerical tasks.
As the extra income generated by these technological advances has been recycled into the economy, new demand for human labour has been generated and there have, generally, still been plenty of jobs to go round.
But a new generation of smart machines, fuelled by rapid advances in artificial intelligence (AI) and robotics, could potentially replace a large proportion of existing human jobs. While some new jobs would be created as in the past, the concern is there may not be enough of these to go round, particularly as the cost of smart machines falls over time and their capabilities increase.
Is artificial intelligence replacing jobs?
There is an element of truth in this argument, and indeed our own past research suggests that up to 30% of existing jobs across the OECD could be at potential risk of automation by the mid-2030s.
Firstly, just because a job has the technical potential to be automated does not mean this will definitely happen. There is a variety of economic, political, regulatory and organizational factors that could block or at least significantly delay automation. Based on our probabilistic risk analysis, our central estimate is that only around 20% of existing UK jobs may actually be displaced by AI and related technologies over the 20 years to 2037, rising to around 26% in China owing to the higher potential for automation there particularly in manufacturing and agriculture. We refer to this as the ‘displacement effect’.
Secondly, and more importantly, AI and related technologies will also boost economic growth and so create many additional job opportunities, just as other past waves of technological innovation have done from steam engines to computers. In particular, AI systems and robots will boost productivity, reduce costs and improve the quality and range of products that companies can produce.
Successful firms will boost profits as a result, much of which will be reinvested either in those companies or in other businesses by shareholders receiving dividends and realising capital gains. To stay competitive, firms will ultimately have to pass most of these benefits on to consumers in the form of lower (quality-adjusted) prices, which will have the effect of increasing real income levels. This means that households can buy more with their money and, as a result, firms will need to hire additional workers to respond to the extra demand. We refer to this as the income effect, which offsets the displacement effect on jobs.
How AI can both destroy and create jobs through the displacement and income effects (this is a simplified analysis – in practice there will be a more complex range of economic effects at work as captured in our detailed modelling) Image: PwC
Our new research put some numbers on these job displacement and income effects for the UK, which we have found from past research is fairly typical of OECD economies as a whole; and China, the largest of the emerging economies.
Estimated displacement, income effect and net effect of AI and related technologies on jobs in China and the UK over the next 20 years Image: PwC
For the UK, the estimated net impact on jobs is broadly neutral, with around 7 million jobs (20%) projected to be displaced in our central scenario but a similar number of new jobs being created. More detailed analysis suggests significant net job gains in sectors like healthcare, where demand will rise due to an ageing population but where there are also limits to the scope for automation because of the continued need for a human touch. Significant job displacement in areas like manufacturing and, as driverless vehicles roll out across the economy, transport and logistics will offset these gains.
For China, there is an estimated negative net impact on agricultural employment, continuing a long-standing trend, more than offset by large increases in construction and services. As for the UK, healthcare will be one area with considerable potential for net job gains given China’s rapidly ageing population.
Estimated net effect of AI on jobs by industry sector in China (millions and %) Image: PwC
One result that might seem surprising is that the impact on jobs in China’s industrial sector is estimated to be broadly neutral. This reflects the fact that while there will be considerable scope for further automation in Chinese manufacturing as wages there rise, we also estimate that China will take the lead in manufacturing the AI-enhanced products (robots, driverless vehicles, drones etc) that will come out of this Fourth Industrial Revolution.
More generally, the huge boost to the Chinese economy from AI and related technologies, which we estimate could be more than 20% of GDP by 2030, will raise real incomes across the economy. This will create new demand for goods and services that will require additional human workers to produce, particularly in areas that are harder to automate.
No room for complacency – the challenge for government and business
While our estimates suggest that fears of mass technological unemployment are probably unfounded, this is not a recipe for complacency. As with past industrial revolutions, this latest one will bring considerable disruption to both labour markets and existing business models.
In China, we could see around 200 million existing jobs displaced over the next two decades, which will require workers to move to industry sectors and places where new jobs will be created. Of course, China has seen even larger movements of workers from the farms to the cities since the early 1980s, but the process will not be easy. Given China’s ageing population, an increase in immigration may be required to meet the demand for additional workers.
Both government and business have a role in maximizing the benefits from AI and related technologies while minimizing the costs. The latter will require increased investment in retraining workers for new careers, boosting their digital skills but also reframing the education system to focus on human skills that are less easy to automate: creativity, co-operation, personal communication, and managerial and entrepreneurial skills. Businesses too have a role to play in encouraging a culture of lifelong learning amongst their workers.
For government, AI will boost economic growth and, therefore, tax revenues. This should enable social safety nets, including state health and social care systems, to be strengthened for those who find it difficult to adjust to the new technologies. Such measures will be important if the huge potential benefits of AI and related technologies are to spread as widely as possible across society.
| 2018-09-18T00:00:00 |
https://www.weforum.org/stories/2018/09/is-artificial-intelligence-replacing-jobs-truth/
|
[
{
"date": "2018/09/18",
"position": 36,
"query": "AI replacing workers"
},
{
"date": "2018/09/18",
"position": 34,
"query": "AI replacing workers"
},
{
"date": "2018/09/18",
"position": 34,
"query": "AI replacing workers"
},
{
"date": "2018/09/18",
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"query": "AI replacing workers"
},
{
"date": "2018/09/18",
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"query": "AI replacing workers"
},
{
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"query": "AI replacing workers"
},
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"date": "2018/09/18",
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},
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},
{
"date": "2018/09/18",
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"query": "AI replacing workers"
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{
"date": "2018/09/18",
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},
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"query": "AI replacing workers"
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{
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},
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"date": "2018/09/18",
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"query": "AI replacing workers"
},
{
"date": "2018/09/18",
"position": 33,
"query": "AI replacing workers"
},
{
"date": "2018/09/18",
"position": 33,
"query": "AI replacing workers"
},
{
"date": "2018/09/18",
"position": 36,
"query": "AI replacing workers"
},
{
"date": "2018/09/18",
"position": 33,
"query": "AI replacing workers"
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|
Why Amazon's Automated Hiring Tool Discriminated Against Women
|
Why Amazon’s Automated Hiring Tool Discriminated Against Women
|
https://www.aclu.org
|
[
"Rachel Goodman",
"Former Staff Attorney",
"Aclu Racial Justice Program"
] |
We've seen these types of problems with artificial intelligence in many other contexts. For example, when we used Amazon's facial ...
|
In 2014, a team of engineers at Amazon began working on a project to automate hiring at their company. Their task was to build an algorithm that could review resumes and determine which applicants Amazon should bring on board. But, according to a Reuters report this week, the project was canned just a year later, when it became clear that the tool systematically discriminated against women applying for technical jobs, such as software engineer positions.
It shouldn’t surprise us at all that the tool developed this kind of bias. The existing pool of Amazon software engineers is overwhelmingly male, and the new software was fed data about those engineers’ resumes. If you simply ask software to discover other resumes that look like the resumes in a “training” data set, reproducing the demographics of the existing workforce is virtually guaranteed.
In the case of the Amazon project, there were a few ways this happened. For example, the tool disadvantaged candidates who went to certain women’s colleges presumably not attended by many existing Amazon engineers. It similarly downgraded resumes that included the word “women’s” — as in “women’s rugby team.” And it privileged resumes with the kinds of verbs that men tend to use, like “executed” and “captured.”
Fortunately, Amazon stopped using the software program when it became clear the problem wasn’t going to go away despite programmers’ efforts to fix it. But recruiting tools that are likely similarly flawed are being used by hundreds of companies large and small, and their use is spreading.
HOW ARTIFICIAL INTELLIGENCE IS CHANGING THE WORKPLACE
There are many different models out there. Some machine learning programs — which learn how to complete a task based on the data they’re fed — scan resume text, while others analyze video interviews or performance on a game of some kind. Regardless, all such tools used for hiring measure success by looking for candidates who are in some way like a group of people (usually, current employees) designated as qualified or desirable by a human. As a result, these tools are not eliminating human bias — they are merely laundering it through software.
And it’s not just gender discrimination we should be concerned about. Think about all the ways in which looking at resume features might similarly cluster candidates by race: zip code, membership in a Black student union or a Latino professional association, or languages spoken. With video analysis, patterns of speech and eye contact have cultural components that can similarly lead to the exclusion of people from particular ethnic or racial groups. The same goes for certain physical or psychological disabilities.
We’ve seen these types of problems with artificial intelligence in many other contexts. For example, when we used Amazon’s facial recognition tool to compare members of Congress against a database of mugshots, we got 28 incorrect matches — and the rate for false matches was higher for members of color. This is due, in part, to the fact that the mugshot database itself had a disproportionately high number of people of color because of racial biases in the criminal justice system.
These tools are not eliminating human bias — they are merely laundering it through software.
Algorithms that disproportionately weed out job candidates of a particular gender, race, or religion are illegal under Title VII, the federal law prohibiting discrimination in employment. And that’s true regardless of whether employers or toolmakers intended to discriminate — “disparate impact discrimination” is enough to make such practices illegal.
But it can be difficult to sue over disparate impact, particularly in “failure-to-hire” cases. Such lawsuits are very rare because it’s so hard for someone who never got an interview to identify the policy or practice that led to her rejection.
That’s why transparency around recruiting programs and other algorithms used by both companies and the government is so crucial. Many vendors who market these hiring tools claim that they test for bias and in fact are less biased than humans. But their software is proprietary, and there’s currently no way to verify their claims. In some cases, careful work by outside auditors may be able to uncover bias, but their research is thwarted by various obstacles. We’re challenging one such obstacle — a federal law that can criminalize testing of employment websites for discrimination.
But even this kind of outside research can’t give us the full picture. We need regulators to examine not only the software itself but also applicant pools and hiring outcomes for companies that deploy the software. The Equal Employment Opportunity Commission, the federal agency that enforces laws against job discrimination, has begun to explore the implications of algorithms for fair employment, and we urge the agency to do more. EEOC should issue guidance for employers considering using these tools, detailing their potential liability for biased outcomes and steps they can take to test for and prevent bias. It should also include questions about data-driven bias in all of its investigations.
Big-data algorithms will replicate and even magnify the biases that exist in society at large — unless they are designed and monitored very, very carefully. The right kind of oversight is required to make sure that happens.
| 2018-10-12T00:00:00 |
2018/10/12
|
https://www.aclu.org/news/womens-rights/why-amazons-automated-hiring-tool-discriminated-against
|
[
{
"date": "2018/10/12",
"position": 97,
"query": "AI hiring"
},
{
"date": "2018/10/12",
"position": 97,
"query": "AI hiring"
},
{
"date": "2018/10/12",
"position": 96,
"query": "AI hiring"
}
] |
Is a universal basic income the answer to robot job loss?
|
Is a universal basic income the answer to robot job loss?
|
https://www.pwc.com.au
|
[] |
While robots won't take our jobs anytime soon, the ways that we work are changing with AI and automation. · A universal basic income (UBI) would ...
|
What will we do when the robots take our jobs?
Ripped from the pages of science fiction dystopias, it’s a familiar refrain as artificial intelligence and automation continue to march on to ubiquity.
But is it a fair one? And if robots do purloin employment opportunities, what will humans do to survive? Enter the universal basic income (UBI), a concept that posits humans should be paid a wage to cover their basic needs, such as food and housing, regardless of their employment status.
It’s an intriguing and often controversial idea, but is it economically possible? And does it address the realities of a world where employment and life’s meaning are often interlinked?
The reality of job loss and the need for a UBI
PwC research indicates that AI and automation will contribute 14% of global GDP by 2030. But with significant amounts of workers worried about losing their jobs, the march towards automation is not seen by all as good news.1
In Will robots really steal our jobs?, PwC predicts three waves of automation implementation: algorithmic, augmentation and autonomy. The first (current) wave, involves automation of simple computational tasks and data analytics. The second wave will see the automation of routine jobs by the mid 2020s. And lastly, by the mid 2030s, automation will replace manual and physical jobs.
These phases will replace varying proportions of jobs in countries at different times, depending on industry makeup. While the report only looks at the feasibility of automation, not the context surrounding it which could alter uptake, it nevertheless calculates that between 22% and 44% of today’s current jobs could be automated by the 30s.
The authors stress that they “do not believe, contrary to some predictions, that automation will lead to mass technological unemployment.” In part, this is because technology will create new jobs even as it changes or replaces old ones. Reskilling, legislation and education will also address changes to the labour market, but so too could other social apparatuses, such as a UBI.
Money for nothing?
In order to live, not to mention keep the economy going, people need money, and a universal basic income could provide it. While the concept is simple – giving people unconditional income – it means different things to different people.
Those skeptical of its feasibility believe that a UBI, seen often as another form of welfare, will place an unaffordable burden on government and reward those who do not need help (ie the employed). Yet a number of academics and high profile entrepreneurs (Elon Musk, Richard Branson, Mark Zuckerberg and Bill Gates* amongst them) believe a UBI is possible, and is perhaps even necessary. Altruists argue that a UBI should be thought of as a universal human right to economic security.
Could it be affordable? Perhaps. A basic income could be introduced small and increased over time, or phased in by groups selected by need.2 Moreover, proponents argue, the UBI is often represented as a gross monetary equation, when in fact it could be a net transfer from interactions with existing tax systems – and thus be significantly less expensive.3
These calculations are also often made off today’s economic reality. PwC predicts that automation will lead to a significant injection of money into global GDP (an amount of around US$15 trillion in today’s money by 2020) and as Elon Musk puts it, automation leads to abundance. In such a world, a UBI could be both affordable and seen as fair redistribution of technological wealth.4
Will a guaranteed income lead to unemployment?
Some argue that a UBI would prove a disincentive to work. One view however is that unlike many welfare programs, a UBI will not penalise someone – by taking away their welfare money – for getting a job.5 In this way, employment becomes an additional source of income, not a competing one. On the other hand a UBI might encourage those who are working to stop doing so, given their higher income taxes could potentially be paying other people’s guaranteed income.6
Importantly, studies thus far have not found a correlation between UBI and unemployment. In fact, they’ve often shown the opposite. The city of Dauphin in Manitoba, for example, found that people were not dissuaded from employment when receiving a UBI, and the only two groups who worked less were mothers with young children, and teenagers who remained in school longer.7
A human sense of purpose
In the short term, it is unlikely the world will see mass job loss from automation. But what if it’s a possibility in the long term?
There’s little question that for many, employment provides a sense of purpose and societal usefulness. As Musk remarked, without jobs, where will people find their meaning? Potentially, in innovation. Mark Zuckerberg acknowledged in his UBI commentary that while innovation and wealth comes in part from hard work, it also comes from the luck of circumstance – the ability to work on ideas which may not make money with the safety net of underlying economic security.8
It’s worth noting of course, that there are plenty of people in the world who currently do unpaid work (parenting, unpaid labour, caregiving) and a UBI would be a fair way to acknowledge (and encourage) these contributions. And, as UBI advocate Scott Santen notes, creative work, often thought to be the last non-automatable work, is often intrinsically motivated. Meaning, therefore, could be found through unemployed ‘work’ of a different sort. Says Santens, “It’s the difference between doing meaningless work for more money, and using money to do meaningful work.”9
Universal equality
While UBI might be suggested as a solution to a joblessness problem, there are other benefits to such a scheme. Raising people above the poverty line has, studies show, a marked positive effect to individuals, families, communities and society.
In Dauphin, researchers found that after UBI implementation hospitalisation rates went down by 8.5%, particularly with regards to mental health diagnoses.10,11 In a Namibian experiment, crime dropped 42% and in Mexico, children experienced a reduction in behavioural problems and better childhood development.12 Communities in general seem to experience better social cohesion with the removal of money-related psychological burdens.13
The effect of shared profit from a local casino to Cherokee Indian tribal members was a 40% decrease in children’s behavioural problems. Indeed, the younger children were when the casino opened, the better they were with regards to emotional problems and drug and alcohol issues later in life.14
Not to mention the saving in cost to government of no longer needing to support those with insufficient income and its correlated effects on social infrastructure such as the health, education or justice systems.
A physical solution to a digital consequence
Much of the rhetoric around the feasibility of a universal basic income is ideologically-based. Do humans deserve money as a basic right or should they only receive it if they’ve ‘earned it’? Depending on political leanings, cultural sensitivities and upbringing, people have polarised UBI views.
Yet, with an uncertain future, it seems foolhardy to dismiss the idea out of hand, particularly when there are so many ways in which it could be designed, implemented, paid for and benefit society. As in previous industrial revolutions, fears of job loss may prove unfounded. Yet there can be no doubt that we are to encounter a change in the way that we work as a result of digital innovation, and the consequences of those changes are unclear.
While we prepare for a future where moving parts require constant reskilling to remain relevant, and emerging technologies create new and fascinating possibilities, the idea of a universal basic income – however it might look – remains a comforting safety net in case we lose our balance.
| 2018-10-24T00:00:00 |
https://www.pwc.com.au/digitalpulse/universal-basic-income-robot-job-loss.html
|
[
{
"date": "2018/10/24",
"position": 53,
"query": "universal basic income AI"
},
{
"date": "2018/10/24",
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"query": "universal basic income AI"
},
{
"date": "2018/10/24",
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"query": "universal basic income AI"
},
{
"date": "2018/10/24",
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"query": "universal basic income AI"
},
{
"date": "2018/10/24",
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"query": "universal basic income AI"
},
{
"date": "2018/10/24",
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"query": "universal basic income AI"
},
{
"date": "2018/10/24",
"position": 53,
"query": "universal basic income AI"
},
{
"date": "2018/10/24",
"position": 51,
"query": "universal basic income AI"
},
{
"date": "2018/10/24",
"position": 49,
"query": "universal basic income AI"
},
{
"date": "2018/10/24",
"position": 53,
"query": "universal basic income AI"
},
{
"date": "2018/10/24",
"position": 56,
"query": "universal basic income AI"
},
{
"date": "2018/10/24",
"position": 55,
"query": "universal basic income AI"
}
] |
|
In the AI world journalism is so important - State of Digital Publishing
|
In the AI world journalism is so important
|
https://www.stateofdigitalpublishing.com
|
[
"Shelley Seale",
"Vahe Arabian",
"Founder",
"Editor In Chief Of State Of Digital Publishing. My Vision Is To Provide Digital Publishing",
"Media Professionals A Platform To Collaborate",
"Promote Their Efforts",
"My Passion Is To Uncover Talent",
"Read More"
] |
Many journalists today are worried about what Artificial Intelligence (AI) means for their job security. OSF says that AI can work with ...
|
What’s happening:
Many journalists today are worried about what Artificial Intelligence (AI) means for their job security. With computers generating a vast array of content now — from weather and stock exchange activity to sports and corporate performance — AI can often produce more rigorous, comprehensive stories than human reporters. Software can instantaneously source data from multiple sources, recognise patterns, and construct complex written stories that even capture emotion.
Yet, rather than fearing that AI will leave them without a job, journalists should embrace it as a saviour of the media trade, says the Open Society Foundation (OSF). Intelligent machines can work together with human journalists, enabling them to better cover the increasingly complex, information-rich world and turbo-power their creativity, reporting, and ability to engage their audiences.
Benefits of AI to media:
OSF says that AI can work with human journalists to increase the quality of media coverage in a variety of ways:
Following predictable data patterns and programmed to “learn” variations in these patterns over time, AI algorithms can help reporters arrange, sort, and produce content at speeds never thought possible.
AI can systematise data to find a missing link in an investigative story.
AI can identify trends and spot the outlier among millions of data points, enabling journalists to uncover the beginnings of a great scoop.
AI can analyse huge amounts of data to aid timely investigations, and can also help source and fact-check stories.
AI algorithms can also help journalists make rough cuts of videos, recognise voice patterns, identify a face in the crowd, and chat with readers.
Limitations of AI
As OSF says, even with all of this technology that AI offers, it still has limitations that require human interaction. The entire process cannot happen without a human journalist who can interpret and ask relevant questions about the data. Collaboration is the answer, with plenty of learning from both sides and some unavoidable trial and error.
The use of AI technology can be a major benefit to journalists around the world, who often don’t have access to such data and programming. Small newsrooms and freelancers can make up for this lack of resources by teaming up with software developers and taking advantage of the many open-source search and analytics tools available.
Ethical challenges
In this collaboration between technology and journalism, however, some ethical considerations arise. Algorithms can lie or be misleading, because they don’t exist in a vacuum — they have been programmed by humans, who may have imparted their own biases and logic patterns into the system. Journalists still need to use old-fashioned source-verification and fact-checking work with AI findings, just as with anything else. The Guardian, for example, proposed a new clause in the newspaper’s code of ethics addressing the use of AI.
Transparency is another ethical issue. This basic tenet of journalism is often at odds with AI, which usually works behind the scenes, says Nausicaa Renner, digital editor of the Columbia Journalism Review. The media needs to be transparent about disclosing what personal data it is collecting, and be careful of catering so stringently to each reader’s own personal taste, as revealed by the data, that they miss reporting on important public issues.
The bottom line:
AI can enable journalism as never before, but it also brings new challenges for learning and accountability. Rather than fearing AI, journalists can use the technology to improve their reporting; yet, they must be transparent about how they are using algorithms to find patterns or process evidence for a story. And, healthy journalism shouldn’t rely on the data provided by AI, but continue to tell the stories that aren’t uncovered by technology and data.
“Without ethics, intelligent technology could herald journalism’s demise,” wrote Maria Teresa Ronderos, director of the Program on Independent Journalism. “Without clear purposes, transparent processes and the public interest as a compass, journalism will lose the credibility of people, no matter how many charts, bots and whistles you adorn it with.”
| 2018-11-01T00:00:00 |
2018/11/01
|
https://www.stateofdigitalpublishing.com/content-strategy/in-the-ai-world-journalism-is-so-important/
|
[
{
"date": "2018/11/01",
"position": 77,
"query": "artificial intelligence journalism"
}
] |
Share of jobs at high risk of automation by region and industry by 2030
|
Jobs at high risk of automation by 2030
|
https://www.statista.com
|
[
"Ahmed Sherif",
"Jul"
] |
percent of jobs in the energy, utilities and mining industry in North America are at high risk of automation. Read more. Show all numbers.
|
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Statista. (November 13, 2018). Share of jobs at high risk of automation by 2030, by region and industry sector [Graph]. In Statista . Retrieved July 14, 2025, from https://www.statista.com/statistics/941743/jobs-at-high-risk-of-automation-by-2030-region-and-industry/
Statista. "Share of jobs at high risk of automation by 2030, by region and industry sector." Chart. November 13, 2018. Statista. Accessed July 14, 2025. https://www.statista.com/statistics/941743/jobs-at-high-risk-of-automation-by-2030-region-and-industry/
Statista. (2018). Share of jobs at high risk of automation by 2030, by region and industry sector . Statista . Statista Inc.. Accessed: July 14, 2025. https://www.statista.com/statistics/941743/jobs-at-high-risk-of-automation-by-2030-region-and-industry/
Statista. "Share of Jobs at High Risk of Automation by 2030, by Region and Industry Sector." Statista , Statista Inc., 13 Nov 2018, https://www.statista.com/statistics/941743/jobs-at-high-risk-of-automation-by-2030-region-and-industry/
Statista, Share of jobs at high risk of automation by 2030, by region and industry sector Statista, https://www.statista.com/statistics/941743/jobs-at-high-risk-of-automation-by-2030-region-and-industry/ (last visited July 14, 2025)
Share of jobs at high risk of automation by 2030, by region and industry sector [Graph], Statista, November 13, 2018. [Online]. Available: https://www.statista.com/statistics/941743/jobs-at-high-risk-of-automation-by-2030-region-and-industry/
| 2018-11-13T00:00:00 |
https://www.statista.com/statistics/941743/jobs-at-high-risk-of-automation-by-2030-region-and-industry/
|
[
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"query": "job automation statistics"
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{
"date": "2018/11/13",
"position": 41,
"query": "job automation statistics"
},
{
"date": "2018/11/13",
"position": 41,
"query": "job automation statistics"
},
{
"date": "2018/11/13",
"position": 41,
"query": "job automation statistics"
},
{
"date": "2018/11/13",
"position": 39,
"query": "job automation statistics"
},
{
"date": "2018/11/13",
"position": 40,
"query": "job automation statistics"
},
{
"date": "2018/11/13",
"position": 41,
"query": "job automation statistics"
}
] |
|
Automation and job displacement in emerging markets: New evidence
|
Automation and job displacement in emerging markets: New evidence
|
https://cepr.org
|
[] |
A first finding is that labour in emerging markets is significantly less exposed to routinisation than labour in developed countries.
|
Concerns about the dislocation of workers are widespread in developed economies, where the recent focus has been on labour market polarisation – the phenomenon of rising wages and employment gains for high- and low-skilled labour relative to middle-skilled labour (Autor and Dorn 2013, Goos et al. 2014). The leading explanation that has emerged to explain polarisation is ‘routinisation’. Middle-skilled workers perform routine tasks easily automated by information and computer technology (ICT), which has resulted in their pronounced job displacement and weak wage growth as the real price of computer capital has declined precipitously (Acemoglu and Autor 2011).
Evidence that routinisation lies behind polarisation has been documented for many developed economies, among them the US, Japan, and a number of western European economies (e.g. Spitz-Oener 2006, Autor and Dorn 2013, Michaels et al. 2013, Goos et. al. 2014, Ikenaga and Kamibayashi 2016). To date, however, little is known about the incidence of routinisation in emerging markets, and whether the worldwide diffusion of technology has – or will – result in polarising their labour markets. As hosts to a sizable fraction of the global labour force, the implications of routinisation in emerging markets for jobs, growth, and inequality could be profound, bringing with it premature deindustrialisation and disrupting income convergence (Rodrik 2016, Berg et al. 2018, IMF 2018).
How exposed are jobs in emerging markets to automation by ICT? What current indicators of the labour market can we draw on to assess their prospects for polarisation? What do recent trends hold for the erosion of middle-skilled jobs in these economies? The answers to such questions are highly relevant for the design of education, industrial, and public welfare policies in emerging markets.
The exposure to routinisation in emerging and developed economies
In a new paper, Benjamin Hilgenstock and I propose a measure of the exposure to routinisation – that is, the risk of labour displacement by information technology – that can help answer such questions (Das and Hilgenstock 2018). Combining the intrinsic potential for routinisability of an occupation with its employment share, the exposures measure how intensive a country is in the labour input of routine tasks, and thus the extent to which jobs are at risk of being routinised. Drawing on national censuses and labour surveys for 160 countries between 1960 and 2015, we establish new facts about the exposures to routinisation around the world, uncovering systematic differences between the level and dynamics of exposures in emerging and developed economies, and draw out their implications for polarisation.
A first finding is that labour in emerging markets is significantly less exposed to routinisation than labour in developed countries and remained less exposed for the quarter-century between 1990 and 2015. This finding is consistent with the widely held view that production is less capital-intensive in emerging markets, where jobs are concentrated in manual, low-skilled tasks intrinsically indisposed to automation.
Second, the initial exposure to routinisation contains important signals about the long-run shifts in the distribution of routine employment and thus the prospects for polarisation. However, we identify a sharp asymmetry by stage of economic development: among developed economies, which were heavily exposed to routinisation to start with, the higher the initial exposure, the lower the subsequent exposure. But among emerging economies, which had initially low exposures to routinisation, the higher the initial exposure, the lower the subsequent rise in exposure. Figure 1 illustrates this striking difference in the evolution of exposures since 1990.
Figure 1 Evolution of the exposure to routinisation, 1990-2015
Notes: Initial routine exposure is measured in 1990-95. Change in routine exposure is the latest exposure in 2010-15 less initial exposure.
Sources: see Das and Hilgenstock (2018).
What lies behind these divergent evolutions of the exposure to routinisation and what do they portend? In developed economies, where initial exposures were high, consistent with the literature we find that the falling price of automating technology – along with the offshoring of routine-intensive jobs to cheap-labour locations – led to an intense displacement of routine labour, making the marginal task less routine, thus lowering routine exposure. Both dimensions matter: in countries where initial exposures were about equivalent, those which experienced greater declines in the price of capital also saw greater declines in exposures, while among countries which faced about the same decline in the price of capital, those with higher initial exposures generally found themselves subsequently less exposed to routinisation.
In emerging markets, by contrast, the price of capital experienced little to no decline in this period and we identify two other forces behind rising exposures to routinisation: the ongoing structural transformation of these economies, and the globalisation of trade. Structural transformation has moved labour from non-routine jobs in agriculture to routine-intensive manufacturing and service jobs (Bárányi and Siegel 2018), and this phenomenon has been compounded by the surge in offshoring which has transferred routine-intensive factory and service jobs from developed countries to their developing counterparts (Blinder and Krueger 2013, Lian 2018). These findings are new to the literature on polarisation and present an interesting contrast to the well-researched forces behind routinisation in developed economies.
Implications of rising exposures to routinisation in emerging markets
An important consequence of the asymmetric evolutions of the exposure to routinisation in developed versus emerging markets is a worldwide convergence in exposures, as the exposure to routinisation in emerging economies rise toward levels once seen in developed nations (Figure 2).
Figure 2 Trends in exposure to routinisation across the world
Source: Data from Das and Hilgenstock (2018).
What do these trends hold for the future of labour markets in emerging markets? An immediate implication is that their labour markets are becoming increasingly exposed to technological disruptions. The lessons from developed economies suggests that rising exposure to routinisation has the potential to trigger labour market dislocations on a significant scale, especially considering the pace at which some large emerging markets are already automating production. Further declines in the cost of capital may also induce developed economies to automate rather than offshore jobs, reinforcing job losses that could arise from automation in emerging markets. Such technological dynamics could erode middle-skilled employment earlier in the convergence process than in developed economies, bringing it with premature deindustrialisation (Maloney and Molina 2016, Rodrik 2016).
However, our analysis underscores that such dislocations are likely to be episodic in the near-term, sporadically arising in some large manufacturing industries in a few large emerging markets, but not widespread to disrupt emerging labour markets on a macro-significant scale over at least the next five years. This is because rising exposure to routinisation does not in and of itself precipitate job losses without a clear pecuniary trigger such as a sharp drop in the relative price of capital, and the ongoing offshoring of jobs (though slowing) continues to raise demand for routine labour in emerging markets. Furthermore, although a few export hubs in Asia are experiencing wage escalation, in emerging markets as a whole the price of investment goods is forecasted to remain contained and significantly higher than labour and other factor costs for some time to come (Das and Hilgenstock 2018).
Conclusions
In this column we combine data on the routine-intensity of occupations with the occupation distribution of employment to construct a country’s exposure to routinisation, and illustrate that the initial exposure to routinisation has predictive power for future labour market developments Drawing on a cross-country time series of exposures, our analysis suggests that although large-scale labour market dislocation is not imminent, emerging markets are becoming increasingly exposed to routinisation – and thus labour market polarisation – from the long-term effects of structural transformation and the onshoring of routine-intensive jobs.
The database on exposures to routinisation has widespread applicability on topics beyond labour market polarisation. As the exposure to routinisation in an initial period is exogenous to subsequent shocks, the cross-country heterogeneity of exposures provides important variation in answering questions about the long-run causal impact from technological advancements in information technology on macroeconomic outcomes. These data have recently been used to quantify the impact of the initial exposure to routinisation on the labour share of income (Dao et. al. 2017), labour force participation (Grigoli et al. 2018), middle-class income (Nakamura 2018), and youth unemployment (Ahn et. al. forthcoming).
Author’s note: The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management.
References
Acemoglu, D and D H Autor (2011), “Skill, Tasks and Technologies: Implications for Employment and Earnings”, in Handbook of Labor Economics, Volume 4, Elsevier.
Ahn,J, Z An, J Bluedorn, G Ciminelli, Z Koczan and D Muhaj (forthcoming), “Youth Labor Market Prospects in Emerging Markets and Developing Economies: Drivers and Policies”, IMF Staff Discussion Note.
Autor, D H and D Dorn (2013), "The growth of low-skill service jobs and the polarization of the US labor market", American Economic Review 103(5): 1553–1597.
Bárányi, Z and C Siegel (2018), “Job polarization and structural change”, American Economic Journal: Macroeconomics 10: 57-89.
Berg, A, E Buffie and F Zanna (2018), “Should We Fear the Robot Revolution? (The Correct Answer is Yes)”, IMF Working Paper WP18/116.
Blinder, A and A Krueger (2013), "Alternative Measures of Offshorability: A Survey Approach," Journal of Labor Economics 31(S1): S97 - S128.
Dao, M, M Das, Z Koczan and W Lian (2017), “Why is Labor Receiving a Smaller Share of Global Income? Theory and Evidence”, Working Paper.
Das, M and B Hilgenstock (2018), “The Exposure to Routinization: Labor Market Implications for Developed and Developing Economies”, IMF Working Paper.
Goos, M, A Manning and A Salomons (2014), “Explaining Job Polarization: Routine-Biased Technological Change and Offshoring”, The American Economic Review 104(8): 2509–2526.
Grigoli, F, Z Koczan and P Topalova (2018), “A Cohort-Based Analysis of Labor Force Participation for Advanced Economies”, IMF Working Paper WP/18/120.
IMF (2018), Technology and the Future of Work.
Ikenaga, T and R Kamibayashi (2016), “Task Polarization in the Japanese Labor Market: Evidence of a Long-Term Trend”, Industrial Relations 55: 267–293.
Lian, W (2018), “Task Trade and Global Labor Share”, Working Paper.
Michael, G, A Natraj, and J Van Reenen (2013), “Has ICT Polarized Skill Demand? Evidence from Eleven Countries over 25 Years”, NBER Working Paper No. 16138.
Maloney, W and C Molina (2016), “Are Automation and Trade Polarizing Developing Country Labor Markets, Too?”, World Bank Policy Research Working Paper 7922.
Nakamura, T (2018), “Why is the American Middle Class Vanishing”, Working Paper, Kobe Gakuin University.
Rodrik, D (2016), "Premature deindustrialization", Journal of Economic Growth 21(1): 1-33.
Spitz‐Oener, A (2006), "Technical Change, Job Tasks, and Rising Educational Demands: Looking outside the Wage Structure", Journal of Labor Economics 24(2): 235-270.
| 2018-11-13T00:00:00 |
https://cepr.org/voxeu/columns/automation-and-job-displacement-emerging-markets-new-evidence
|
[
{
"date": "2018/11/13",
"position": 91,
"query": "automation job displacement"
}
] |
|
The Rise of the Robot Reporter - The New York Times
|
The Rise of the Robot Reporter
|
https://www.nytimes.com
|
[
"Jaclyn Peiser"
] |
As the use of artificial intelligence has become a part of the industry's toolbox, journalism executives say it is not a threat to human ...
|
As reporters and editors find themselves the victims of layoffs at digital publishers and traditional newspaper chains alike, journalism generated by machine is on the rise.
Roughly a third of the content published by Bloomberg News uses some form of automated technology. The system used by the company, Cyborg, is able to assist reporters in churning out thousands of articles on company earnings reports each quarter.
The program can dissect a financial report the moment it appears and spit out an immediate news story that includes the most pertinent facts and figures. And unlike business reporters, who find working on that kind of thing a snooze, it does so without complaint.
Untiring and accurate, Cyborg helps Bloomberg in its race against Reuters, its main rival in the field of quick-twitch business financial journalism, as well as giving it a fighting chance against a more recent player in the information race, hedge funds, which use artificial intelligence to serve their clients fresh facts.
| 2019-02-05T00:00:00 |
2019/02/05
|
https://www.nytimes.com/2019/02/05/business/media/artificial-intelligence-journalism-robots.html
|
[
{
"date": "2019/02/05",
"position": 88,
"query": "artificial intelligence journalism"
}
] |
Reskilling workforces for the age of automation - TechHQ
|
Reskilling the workforces for the age of automation
|
https://techhq.com
|
[
"Anil Prabha",
"Dedicated To The Field Enterprise Technology",
"Anil Has Explored The Realms Of Editorial",
"Corporate Communications",
"Branding To Provide A Holistic Perspective To The Task At Hand.",
".Pp-Multiple-Authors-Boxes-Wrapper.Pp-Multiple-Authors-Layout-Boxed.Multiple-Authors-Target-The-Content .Pp-Author-Boxes-Avatar Img",
"Width",
"Important",
"Height",
"Border-Radius"
] |
... automation (RPA) and artificial intelligence (AI) software to cut back on admin costs and shift the burden of menial, repetitive tasks. On a ...
|
A wave of cross-industry automation is coming, but just when it might take hold is uncertain.
Away from the factory floors, finance and insurance firms have been some of the first to take a leap with robotic process automation (RPA) and artificial intelligence (AI) software to cut back on admin costs and shift the burden of menial, repetitive tasks.
On a wider scale, US think tank Brookings Institution recently predicted 25 percent of jobs would be severely disrupted by automation in anything from “a few years or two decades”.
Opinions on the automation takeover are polarized, and attitudes can change depending on which statistics you examine. On the one hand, the onset of the ‘fourth industrial revolution’ could see at least 44 percent workers in the low-wage and low-skilled sector lose their jobs to robots and AI programs by 2030, according to PwC.
On the other, the same report predicts a US$15 trillion boost to global GDP from AI within the same timescale.
Ultimately, cash rules, and if profits will be more as a result of automation, displacement of certain roles is inevitable. Perhaps then, instead of freezing in the headlights and asking, “will robots take my job?”, our efforts should be focused on how the workforce can adapt to this swiftly approaching transformation.
To this end, the reskilling of existing workers will be fundamental.
The cost of reskilling for automation
According to the World Economic Forum, the US private sector could reskill 25 percent of all workers in disrupted jobs at a cost of US$4.7 billion with a positive cost-benefit balance. With the same assumptions applied, the US government could reskill 77 percent of workers at a cost of US$19.9 billion while generating a positive return in the form of taxes and lower welfare payments.
“A lack of key skills is keeping 79% of CEOs awake at night […] it’s one of their top three worries,” Bhushan Sethi, Partner and Joint Global Leader for People and Organization at PwC US, told TechHQ.
As businesses become more digital, that lack of technological know-how is stifling innovation and raising workforce costs more than expected.
According to Sethi, just under half of the CEOs (46 percent) say their first approach to remedy the issues is by reskilling existing workers. Less than a fifth (18 percent) said they’d focus on hiring from outside their industry.
Outside of the boardroom, employees are responding positively— the management consultancy firm’s Tech at Work report found that most staff would be willing to spend two days per month on training to upgrade their digital skills if offered by their employer.
For executives, the cost and resources required for reskilling staff could be daunting— WEF estimates a cost of US$24K per head to reskill displaced workers in the US but, taken considering the additional costs of severance packages, recruitment for specialized roles, onboarding, and training, it might not be such a large price to pay.
The cultural impact of automation
With profits and remaining competitive front of mind for businesses across all industries and, let’s be honest, the impact on human roles more of an afterthought, Sethi agrees that automation will test the resolve of corporate leaders and challenge them to balance their societal values with profitability.
As such, CEOs will need to be clearer about reskilling for the future and what that really means for those within their organization— which skills will be valued and rewarded, and which will not be needed.
“Without this honest narrative for the future it’s certain that CEO platitudes about reskilling will ring hollow,” said Sethi. “Organizations can’t protect jobs which are made redundant by technology – but they do have a responsibility to their people.”
While automation might seem a logical step for industries— strip out the workers and save costs as a result— Sethi believes the wider cultural impact of the rise of automation will pressure businesses into acting with a degree of relative etiquette. Of course, the nature of that cultural impact is still to be seen.
“Linear predictions don’t cut it in today’s world… that’s why we say organizations should plan for a range of scenarios,“ said Sethi, adding this will be an important organizational skill as “clear tensions [emerge] between societal good and short-termism”.
“The truth is we may see different worlds in different markets or sectors, and organizations will have to navigate that complexity,” he said.
On how governments will approach the issue of automation and possible upheavals to society in the future, Sethi said: “Putting aside the political challenges, which are substantial, it’s an easy financial calculation for governments to make.”
That said, Sethi believes that skill mismatches will have a direct impact on nations’ GDP, tax revenues and social safety net bill.
“If low skilled workers cannot take up needed roles without reskilling, jobs will go unfilled and organizations will be less productive and trade will also generate less tax as well,” he said. Luxembourg, for example, has a ‘Digital Skills Bridge’ initiative, which diverts money spent from dealing with the costs of unemployment to co-funding reskilling.
One thing is certain— automation is approaching at wide-scale— and while we can’t yet predict the pace of the takeover, preparation among both organizations and employees will be vital to survival, longevity, and success in the long term.
Ultimately, the real burden lies on the CIO and IT stakeholders to instigate that preparation, ensuring the measured approach to the organizational adoption of new technology that balances both profitability and the health of the workforce that has built the business so far.
| 2019-02-11T00:00:00 |
2019/02/11
|
https://techhq.com/2019/02/reskilling-workforces-for-the-age-of-automation/
|
[
{
"date": "2019/02/11",
"position": 76,
"query": "reskilling AI automation"
}
] |
Automation and AI will disrupt the American labor force. Here's how ...
|
Automation and AI will disrupt the American labor force. Here’s how we can protect workers
|
https://www.brookings.edu
|
[
"Mark Muro",
"Robert Maxim",
"Jacob Whiton",
"Mark Maccarthy",
"Eduardo Levy Yeyati",
"Xiang Hui",
"Oren Reshef"
] |
A quarter of the US workforce consists of some 36 million people who will be highly exposed to automation, and could suffer displacement as a result.
|
But that doesn’t mean the problem is insignificant. A quarter of the U.S. workforce consists of some 36 million people who will be highly exposed to automation, and could suffer displacement as a result.
Last month, we released research that suggests the next phases of workplace automation should be manageable for most workers, with only a quarter of the American workforce facing “high” exposure to automation technologies in the coming decades.
The clear implication: Don’t expect this issue to sort itself out on its own. Indeed, even the Trump Administration acknowledged as much in its recent executive order on artificial intelligence (AI). While the executive order is light on details, it does direct the intergovernmental Select Committee on Artificial Intelligence to provide recommendations “regarding AI-related educational and workforce development considerations.”
What might some of these considerations look like? In our new report, we offer strategies for making the best of the automation era in the form of five major agendas to help maximize the benefits that automation and AI may bring, while mitigating the potential harms.
First among our proposed strategies is for the nation to run a full-employment economy, and, in general, to embrace growth and technology. In doing so, workers will have an easier time maintaining employment or transitioning from one job to another in conditions of widespread hiring. But beyond that, embracing, rather than resisting, the coming generations of digital technology—from automation and data analytics to various forms of AI—will likely help create new jobs and maintain living standards for many workers. Over the past 30 years, technology has been a significant source of new job creation and opportunity.
Furthermore, new technologies increase the productivity of the workers they do not displace, which in turn raises those workers’ wages and increases demand for other work across the economy. For this reason, the U.S. must step up its funding for R&D on emerging technologies like AI, big data, and super-computing, with an emphasis on leading global efforts to develop these technologies ethically and humanely. Through such investment, the U.S. can promote further job creation while securing global leadership in standard settings. That matters doubly because to cede such leadership to autocratic nations like China would be a disaster for human rights.
But the nation and its workers will need more than just a sufficient rate of job creation to offset the likelihood of job destruction. Even in the best of times, many, if not most, workers will strain to manage the coming necessary adjustments as automation and AI change or eliminate many jobs, while simultaneously creating new ones. In preparation for the changes to come, the nation needs to make a more serious commitment to helping workers and communities adjust, and to reducing hardships for those who are struggling. Here are four priorities:
Promote a constant learning mindset
Nearly all workers are likely to see some task change in their jobs—just think of how the emergence and widespread adoption of the personal computer over the past 30 years has affected nearly every worker. To adapt to coming changes, workers will need more support for skill development. Unfortunately, employer-supported training, one of the main forms of skill development for incumbent workers, has been declining over time:
Furthermore, we no longer have a clear idea of how many workers receive on-the-job training. The U.S. Census Bureau’s Survey of Income and Program Participation, which has provided this data in the past, did not collect any information about on-the-job or employer-sponsored training in its most recent panel in 2014.
But businesses must not be let off the hook—more firms must do their part to offer employer-led trainings or provide tuition or other skill-development benefits. Policymakers, in turn, can take steps to incentivize companies to increase training efforts, such as human development tax credits or lifelong learning accounts. Policymakers should also explore the viability of new learning models such as accelerated learning and experiential learning. And across all levels, education and training efforts should impart durable skills to help individuals both work with machines, as well as do what machines cannot.
Facilitate smoother adjustment
While our analysis shows that just 25 percent of U.S. jobs are highly exposed to automation, that still equates to over 36 million workers. Many of those workers—as well as some workers who are less exposed—may lose their job completely. However, the current worker adjustment system in the United States is wholly inadequate. As a portion of our economy, we spend less than nearly every other industrialized country on so-called active labor market policies (ALMPs) that help train workers and match them to jobs:
Furthermore, we invest less than we used to in these policies—as a percentage of GDP, we spend less than half of what we spent in 1985:
To reverse this trend, policymakers should increase funding for active labor market policies. However, just increasing funding indiscriminately is not enough. To best support worker adjustment, policymakers should create a Universal Adjustment Benefit that would help all displaced workers. Such a program would be anchored by three core components:
Automatic enrollment in career counseling for every displaced worker
Expanded training access for all dislocated workers
Robust income support for workers in training
Reduce hardships for workers who are struggling
Automation and AI will exacerbate financial insecurity by forcing many workers into low-wage work. It will be necessary for policymakers to take steps to reduce financial uncertainty and volatility. Policymakers should expand the earned income tax credit (EITC) (and issue payments quarterly or monthly) and enact a wage insurance program so workers forced into lower paying jobs can better make ends meet. Meanwhile, policies like state-run individual retirement accounts (IRAs), paid sick and family leave for all workers, and public healthcare options can provide workers in low-wage jobs a modicum of financial security.
Mitigate harsh local impacts
For places like the small cities and rural areas that are at the highest risk from automation, even deeper investments will be necessary. Policymakers must help pivot these economies from the industries of the past to the industries of the future. They should boost the adoption of new, intelligent technologies by firms in hard-hit regional economies, as well as expand support for community adjustment efforts. One way to do so would be to provide extensive support for a group of small-to-medium-sized metros to serve as regional “growth poles.” Finally, policymakers and companies must future-proof regional workforces through, for example, specialized training modules that develop the skills that lead to automation-resilient work.
As evidenced by ongoing discussions in the business community, companies have fully committed to a new wave of automation. Doing so makes business sense—it will help bolster firm productivity and improve companies’ bottom lines. However, these decisions will also have significant impacts on the lives of millions of workers. Therefore, policymakers at all levels must step in with new investments to mitigate the worst impacts of automation, and to ensure a just and equitable transition to a 21st century economy. Without urgent and consequential action, we should expect the coming decades to look much like the last: considerable economic benefits for some, but significant strain and disruption for many others.
| 2019-02-25T00:00:00 |
https://www.brookings.edu/articles/automation-and-ai-will-disrupt-the-american-labor-force-heres-how-we-can-protect-workers/
|
[
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"date": "2019/02/25",
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"date": "2019/02/25",
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{
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{
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|
How Globalization and Robotics Speed Up Job Losses
|
How Globalization and Robotics Speed Up Job Losses
|
https://knowledge.wharton.upenn.edu
|
[] |
The twin trends of globalization and robotics could lead to an unprecedented disruption that would displace workers at the fastest pace in history.
|
The twin trends of globalization and robotics — or globotics — will usher in a period of unprecedented disruption that could displace workers at the fastest pace in history, argues Richard Baldwin, international economics professor at the Graduate Institute, Geneva, in his new book, The Globotics Upheaval: Globalization, Robotics and the Future of Work.
Like factory workers who lost their jobs to automation, white-collar and service workers are now in danger of being displaced en masse, said Baldwin, also editor-in-chief of policy website VoxEU.org. He recently joined the Knowledge at Wharton radio show on SiriusXM to discuss this trend and how workers can protect themselves.
An edited transcript of the conversation follows.
Knowledge at Wharton: If automation first diminished blue-collar jobs, wasn’t it inevitable that technology would affect white-collar jobs?
Richard Baldwin: I think it is a natural, but what I would rather focus on is technology. What is really driving this opening up of office jobs and service jobs is that digital technology is getting really good. It is especially important for people working in offices, because that is really about information. Basically, you are moving information around and processing it, and digital technology has really changed the ability to do that over longer distances. Once things get arranged so you can work from home, it is not that big of a jump for somebody to work from a farther away country, charging a much lower salary.
The biggest arbitrage or gain for your employers will be to hire people who can do more or less what you can, but for a tenth of the price. Increasingly, these international freelancers or ‘tele-migrants’ are going to be in developing countries. But I think there will be a bit of a time-zone thing. Quite a few people in South America will provide services in North America, and people in Africa will provide to Europe, and Southeast Asia to East Asia, for example. That is where these tele-migrants are going to be lining up.
Knowledge at Wharton: One of the things that you bring up from a historical perspective is the slowing growth that we have seen in general. Many people will talk about it in the short term because of what we went through a decade ago with the financial crisis and the recession. But you talk about it going back to the early 1970s, when we really started to see the rate of growth slow down.
Baldwin: Growth definitely slowed down in all of the advanced economies around 1970, by about half. The U.S. was growing 3% to 4%, and it started growing 1% to 2%. Japan was growing at 6% to 8%, and it started growing 2% to 4%, and so on. But [no one] is 100% sure what caused that. The employment in U.S. manufacturing peaked right about that time, and it has been declining ever since. It was basically robot arms replacing human hands. The key there was that they invented the computer chip, so you could put a computer on a robot arm and a robot could then do lots of stuff that you used to need human hands for. It started automating away jobs.
“The biggest arbitrage or gain for your employers will be to hire people who can do more or less what you can, but for a tenth of the price.”
Knowledge at Wharton: In what sectors do you see the greatest concern of job loss?
Baldwin: In my book, I talk about automation of service-sector jobs and professional jobs and globalization, or at least tele-migrating stuff. I think the easiest way to do it is just to look around your office and see who is telecommuting. Which parts of which jobs can be done without actually being in the office? Those are the ones that are going to go first. Also, I think it is important to remember that this is not about occupations as a whole, it is about tasks within occupations.
Look at the tractor. … It was a very good tool that changed the nature of the job the farmer was doing, and it meant we needed fewer farmers to do the work. But it didn’t eliminate the occupation of farmers. When you think about people in accounting or IT, or people who manipulate data online, that sort of stuff, those are the jobs that can be automated most quickly. Lots of teams have remote workers, and those remote workers are the ones who are going to go first.
Knowledge at Wharton: You say that this upheaval not only will impact our economies, but it will impact our political systems as well. How so?
Baldwin: I am not predicting we are going to have a huge upheaval. I am just saying it is not at all unlikely that we will have one. Estimates of how much job displacement will happen go from scary and super-fast to reasonable over a long period of time. Honestly, I don’t have anything to add to those experts that I surveyed, and I don’t think they really know. You are just guessing about this complex future.
But it is a serious possibility that the displacement will happen very fast, and that these people working in offices and professional jobs will join hands with the people who have been hurt by competition by China and robots in factories. We could have a mighty upheaval, something like the yellow vest [protests in France], but just much larger and much faster. That is what I am worried about.
Knowledge at Wharton: Amazon is a huge employer worldwide. Is it one of those companies where workers could be replaced?
Baldwin: Amazon is an interesting case. They basically are in the old business of ordering online and delivering. So much of that is very, very physical, and they use tons of AI to make it easier to find customers and whatnot. It’s the warehousing kind of workers in Amazon that are being replaced. Those are more like factory jobs, so that I don’t think is so disruptive. What I am thinking about is people who work in an office, say, dealing with phone subscriptions.
“Which parts of which jobs can be done without actually being in the office? Those are the ones that are going to go first.”
If I email my phone provider, which is Swisscom in Switzerland, that I want to change my subscription to allow for [my traveling to] the U.S. for the next 10 days, then there will be a human at Swisscom who opens up my email, reads it, tries to figure out what I want, and opens up one database to change my subscription, closes that, and opens up the financial billing database to change my billing.
Until very, very recently you absolutely needed to have a human doing that because the computers couldn’t read the email and understand what I wanted. But now there is a whole thing called robotic process automation, which is kind of like digital knowledge workers. The computer opens up the email, reads it, understands what I want, opens up the database to changes of subscription, closes it, changes it to national database, all without humans and 100 times faster and with fewer errors than a human. It is replacement of jobs like that which I think are going to go fastest.
Knowledge at Wharton: You do say that some of these jobs though will be sheltered at least in the short term, correct?
Baldwin: When you think about which jobs will be replaced and which ones won’t, what you have got to focus on is what artificial intelligence cannot do. There is a bunch of reviews of workplace capacity today I can do. There was a very good one done by McKinsey Global Institute last year. If you line up the capacities where AI is very good and less good, the most human tasks are the ones it can’t do. Motivating people, managing people, providing creativity, dealing with unknown situations, applying ethics — things like that require a human touch or human talent. Those are the things that AI can’t do.
This new way of computers learning to think is all based on machine learning, which is programming computers in a radically different way. When we programmed computers before 2016, you had to know step by logical step what it should do in every single situation. You were just writing down a set of instructions for the computer to follow. Now with machine learning, they don’t do it that way. They take a million observations of, let’s say, a cat face, and 10 million not cat faces, and they estimate an enormous statistical model using super amounts of power for it to guess. It uses hundreds of thousands of clues to guess what is a cat, what is not. That model is so complex that even the AI scientists don’t understand what exactly it is using to identify the cat.
“It is a serious possibility that the displacement will happen very fast.”
That is how our brain works for many things. I can tell you how I calculated a 15% tip, but I cannot tell you how I can recognize the thing I am looking at out my window is a car instead of a bus. That new capacity all depends upon that big data set. The question has to be clear, and the outcome has to be clear. Now, think about your job. What parts of your job are the questions not clear and the outcomes not clear? That is what is going to be sheltered by AI.
Knowledge at Wharton: You specifically mention journalism. How will that be affected?
Baldwin: There are already a number of programs that are robo-journalists. They are used routinely in reporting sports scores and stock market results, and especially election results when there are thousands of news stories that come in at the same time. They have a template where there is a great big database on election results, and then this AI machine generates stories for each and every district in a very quick way. The same is true with the sports scores and the stock markets. They take data from a general feed and turn it into a story using artificial intelligence.
Knowledge at Wharton: But that industry and others still would require human thought to process some of the work, correct?
Baldwin: Almost every job has something where it requires a real human to be there. What I am trying to push in my book is that people ought to look at their own job, their own list of chores, and see which could be automated by one of these machine translation things, which could be replaced by somebody on a Skype screen sitting in the office next to you. What you ought to focus on is getting good at the stuff that neither of those can do.
In the jobs of the future, we will be doing what tele-migrants can’t, and we will be doing what AI can’t. So, we ought to think about what they can’t do and focus on building talents in things that they can’t do.
Knowledge at Wharton: This could lead to a lot of people wanting to change jobs if they believe that theirs will be automated at some point, correct?
Baldwin: Absolutely. There are jobs where this is going to come faster and sooner and harder, and those are not the jobs you want to be in. But the idea you should move out [of a job] is a good idea, it is a good thought. But when I think about it, it has got more to do with what we should be getting our children to do and train for. We want to make sure that they don’t train for jobs that are very, very quickly going to disappear. But people who have jobs, you’ve got to think about moving into different things, sheltered jobs.
“Just getting more education is not enough. You have to focus more on the human skills.”
Knowledge at Wharton: Should education be adjusted to accommodate these changes so that young people will be better prepared?
Baldwin: Yes, that is the last part of my book. One of the key rules about getting ready for this is, the old rules don’t work. The old rules for dealing with globalization and automation were get more skills or education and training. Almost universally in Europe, the United States, in families all over the world, they say a kid has got to get more education so they can survive and thrive in this world of globalization. The reason that worked was, essentially, globalization and automation only work in things like manufacturing and farming and mining — industries where you actually do things.
But the more education you get, the more likely you are going to end up in a profession where automation was not working and there was no globalization because of technological barriers. That was not a bad idea for the last time, but this time you are going to have to be a little bit more subtle. You can’t just get more skills, it is going to have to be, which skills. In particular, we are going to have to think about more human skills, softer skills. Of course, everybody will have to have minimum of digital fluency and literacy, but mostly young people already have that. That will be the table stakes in the future market.
After that, managing people was much less replaceable than, for example, drawing architectural plans or looking through legal documents and trying to find evidence. Those are things that robots are starting to get very good at, so just getting more education is not enough. You have to focus more on the human skills.
| 2019-03-13T00:00:00 |
https://knowledge.wharton.upenn.edu/podcast/knowledge-at-wharton-podcast/globotics-upheaval/
|
[
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"position": 45,
"query": "robotics job displacement"
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"date": "2019/03/13",
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{
"date": "2019/03/13",
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"date": "2019/03/13",
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"query": "robotics job displacement"
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"query": "robotics job displacement"
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|
Women face greater threat from job automation than men
|
Women face greater threat from job automation than men
|
https://news.trust.org
|
[
"Thomson Reuters Foundation"
] |
Drawing on data from the Bureau of Labor Statistics and research examining the possibility of automation based on current technology, the ...
|
Among the positions with more than a 90 percent chance of becoming automated are administrative assistant, office clerk, bookkeeper and cashier, all fields dominated by women
By Kate Ryan
NEW YORK, March 13 (Thomson Reuters Foundation) - Women across the economic spectrum are more vulnerable than men to losing their jobs to technology, according to a study released on Wednesday by the Institute for Women's Policy Research.
Among the positions with more than a 90 percent chance of becoming automated were administrative assistant, office clerk, bookkeeper and cashier, all fields dominated by women.
"We're already seeing some of that with tasks being replaced by computers," said Chandra Childers, the study director and a senior researcher at the IWPR.
Drawing on data from the Bureau of Labor Statistics and research examining the possibility of automation based on current technology, the authors found that 58 percent of at-risk workers were women.
For every seven men in occupations with a 90 percent likelihood of automation, there are 10 women.
Researchers noted that while women work in the positions most likely to be automated, they also dominate those at lowest risk for automation, such a childcare and nursing.
These care positions tend to pay $20,000 to $25,000, an annual salary below the poverty line for a family of four and far less than the salaries of male-dominated positions that are not at risk of automation, including executives and legislators.
"We need a push to improve the quality of those jobs," said Childers of care work.
The Bureau of Labor Statistics predicts a jobs increase of 7 percent between 2016 and 2026, and new jobs may be created for the displaced women, Childers said.
Another potential solution for women in at-risk positions would be training for higher-wage positions.
But for women already years or decades into their careers, skill building can be a challenge.
Taking care of children or aging parents, jobs that disproportionately fall on the shoulders of women, leave little time for training, said Childers.
The research does not predict how quickly office automation will take hold, but looks to the jobs where current technology makes automation possible.
Childers said the timeline will largely depend on customers and clients who interact with workers in these positions.
"If people get used to technology, the speed will increase," she said, noting that customers were initially wary of self-checkout machines but now use them regularly.
(Reporting by Kate Ryan; Editing by Jason Fields. Thomson Reuters Foundation, the charitable arm of Thomson reuters, that covers humanitarian news, women's and LGBT+ rights, human trafficking, property rights, and climate chenge. Visit www.trust.org)
Our Standards: The Thomson Reuters Trust Principles.
| 2019-03-13T00:00:00 |
https://news.trust.org/item/20190313214733-dlng5
|
[
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"date": "2019/03/13",
"position": 85,
"query": "job automation statistics"
},
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"date": "2019/03/13",
"position": 86,
"query": "job automation statistics"
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"date": "2019/03/13",
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"position": 87,
"query": "job automation statistics"
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"date": "2019/03/13",
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"query": "job automation statistics"
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] |
|
Employers Embrace Artificial Intelligence for HR - SHRM
|
Employers Embrace Artificial Intelligence for HR
|
https://www.shrm.org
|
[
"Dinah Wisenberg Brin"
] |
More than 40 percent of U.S. employers currently use chatbots to engage with candidates during recruitment and 39 percent use them for employee ...
|
Designed and delivered by HR experts to empower you with the knowledge and tools you need to drive lasting change in the workplace.
Demonstrate targeted competence and enhance credibility among peers and employers.
Gain a deeper understanding and develop critical skills.
| 2019-03-21T00:00:00 |
https://www.shrm.org/topics-tools/news/employers-embrace-artificial-intelligence-hr
|
[
{
"date": "2019/03/21",
"position": 60,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 63,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 59,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 54,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 63,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 57,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 53,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 65,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 58,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 65,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 59,
"query": "artificial intelligence employers"
},
{
"date": "2019/03/21",
"position": 65,
"query": "artificial intelligence employers"
}
] |
|
Which occupations are at highest risk of being automated?
|
Which occupations are at highest risk of being automated?
|
https://www.ons.gov.uk
|
[] |
The ONS has analysed the jobs of 20 million people1 in England in 2017, and has found that 7.4% are at high risk of automation. Automation ...
|
Around 1.5 million jobs in England are at high risk of some of their duties and tasks being automated in the future, Office for National Statistics (ONS) analysis shows.
The ONS has analysed the jobs of 20 million people1 in England in 2017, and has found that 7.4% are at high risk of automation.
Automation involves replacing tasks currently done by workers with technology, which could include computer programs, algorithms, or even robots.
Women, young people, and those who work part-time are most likely to work in roles that are at high risk of automation.
It is important to understand automation as it may have an impact on the labour market, economy and society.
The proportion of jobs at a high risk of automation decreased slightly between 2011 and 2017, from 8.1% to 7.4%, while the proportion of jobs at low and medium risk of automation has risen.
The exact reasons for the decrease in the proportion of roles at high risk of automation are unclear, but it is possible that automation of some jobs has already happened. For instance, self-checkouts at supermarkets are now a common sight, reducing the need to have as many employees working at checkouts. Additionally, while the overall number of jobs has increased, the majority of these are in occupations that are at low or medium risk, suggesting that the labour market may be changing to jobs that require more complex and less routine skills.
Find out more about automation using our chat bot:
Embed code Embed this interactive Copy
The analysis looked at the tasks performed by people in jobs across the whole labour market, to assess the probability that some of these tasks could be replaced through automation.
It is not so much that robots are taking over, but that routine and repetitive tasks can be carried out more quickly and efficiently by an algorithm written by a human, or a machine designed for one specific function. The risk of automation tends to be higher for lower-skilled roles for this reason.
When considering the overall risk of automation, the three occupations with the highest probability of automation are waiters and waitresses, shelf fillers and elementary sales occupations, all of which are low skilled or routine.
The three occupations at the lowest risk of automation are medical practitioners, higher education teaching professionals, and senior professionals of educational establishments. These occupations are all considered high skilled.
Embed code Embed this interactive Copy
The risk of automation changes depending on where you work
This is driven by the types of jobs available in a particular area. Generally, the more jobs that require high-skilled workers in an area, the lower the risk of automation overall.
Discover the risk of automation for occupations where you work:
Embed code Embed this interactive Copy
How does the risk of a job being automated change depending on age and sex?
The ONS analysis shows that 70.2% of the roles at high risk of automation are currently held by women. In addition, people aged 20 to 24 years are most likely to be at risk of having their job automated, when compared with other age groups.
Younger people are more likely to be in roles affected by job automation. Of those aged 20 to 24 years who are employed, 15.7% were in jobs at high risk of automation. The risk of job automation decreases for older workers, and is lowest for workers aged between 35 and 39 years. Just 1.3% of people in this age bracket are in roles at high risk of automation. The risk then increases from the age group 40 to 44 upwards.
Young people are more at risk of job automation
Proportion of people at high risk of automation, by age, 2017, England
Embed code Embed this interactive Copy
This pattern can be explained by the fact that workers naturally obtain further skills and become more knowledgeable in their field as they progress further in their careers. When young workers enter the labour market, they may be entering part-time roles and employed in industries like sales, retail, and other roles where some degree of automation is highly likely. Many young workers may move through a range of roles before settling into a career. In addition, younger workers have more time and opportunity to retrain or change career paths.
We can partially explain the increase in the risk of automation from the age of 35 years with the change in working patterns, particularly for women. From the age of 30 years, more women work part-time, and this increases until women reach the age of 50 years, when it then steadily drops down. People who work part-time are more likely to work in roles at a higher risk of automation, but ultimately your occupation determines the probability of automation, not your working pattern.
Notes
An occupation is considered at high risk of automation when its probability of automation is above 70%.
There is a methodology article available if you want to understand more about the analysis and the methodology.
| 2019-03-25T00:00:00 |
2019/03/25
|
https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/whichoccupationsareathighestriskofbeingautomated/2019-03-25
|
[
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] |
How is AI Recruitment Affecting the Hiring Process?
|
How is AI Recruitment Affecting the Hiring Process?
|
https://funding.ryan.com
|
[
"Ryan",
"Canadian Government Funding Team"
] |
AI can help speed up the recruiting process and improve the timeliness of communication between candidates and recruiters.
|
Recruiting is one of the toughest issues organizations face today and finding the right candidate is crucial for business success. It takes a lot of time and money to be confident with a hiring decision, so if that chosen individual ends up not being the right fit for the role, it can be a detrimental setback to the company. Ironically, artificial intelligence (AI) and machine learning has been sweeping throughout the business world to support the traditional recruitment process and solve problems of human capital management.
While some might be worried that all this technology will hinder the human interaction aspect of the recruitment process, if integrated properly, it can reap benefits.
Given that traditional recruiting measures can be a notorious undertaking, AI can enhance the process and help improve talent acquisition efficiency and effectiveness. It can allow Human Resource managers more time to focus on hiring the right person for the job without the backend manual work. Consider some of the ways AI is affecting the recruitment process.
Ways Artificial Intelligence is Integrating into Recruitment Practices
Some of the most central aspects of the hiring process are to source talent, effectively communicate with candidates, and build a relationship with potential hires. Incorporating artificial intelligence into the recruiting process can help support these values and offer benefits for both the recruiter and the candidate.
In a survey carried out by Entelo, 72% of respondents agreed that automated sourcing would increase their productivity.
It’s important to remember that AI recruitment is available to be a support mechanism. AI allows more time to be spent on the necessary human interaction aspect of recruiting by supporting the hiring process in five key areas:
Hire More Targeted Candidates
With AI recruitment, talent acquisition specialists are now able to target, screen, and source more highly qualified candidates. AI can let them target searches by criteria such as age, industry, location, job title, education, household income, salary, and spending habits – for example, by displaying job ads to people with the right profile and interest level (instead of the casual “wide net” approach) and implementing AI to scan and screen existing talent databases, saving time.
Eliminate Hiring Bias
Although it would be ideal to hire someone based only on their ability to do the job well, because everyone is human, there are times where outside factors shield judgment and unconscious decisions are made. The implementation of AI in the recruitment process can help eliminate hiring that is based solely on a resume and intuition. It’s important to note that not all technology is an end-all solution so it’s best to combine the data with your judgment later in the process.
Video Interviewing
There are video interviewing solutions available to read semantics, body language, and grammar and speech patterns which can help reveal a plethora of undeclared information about a candidate. This automated approach is better suited for optimizing high-volume hiring cycles. Video interviewing also speeds up the process for candidates so that they don’t have to worry about the logistics of getting to an office on time. It’s important to be cautious with this approach as video interviewing in general can be intimidating for candidates.
Time Savings
There’s no doubt that an AI-enabled recruitment process would save time. On average, between assessing resumes, scheduling interviews, and emailing candidates, talent acquisition specialists spend nearly 13-20 hours a week sourcing candidates for a single role. Tasks accomplished by AI schedulers, chat-bots, and assessment tools don’t require human intervention, and the technology can handle a much larger load of data. Applying artificial intelligence tools improves the experience for candidates by providing them with timely responses to questions or enquiries they might have and reducing their waiting time.
Manage Candidates’ Timelines
As mentioned, AI can help speed up the recruiting process and improve the timeliness of communication between candidates and recruiters. Applicants appreciate timely responses; 40% of candidates expect to be asked to interview within a week of submitting an application. Sensible communication with potential candidates speaks volumes about your business and a bad recruitment experience may have a negative effect on people’s perception of your company. One way AI can help with this is to have an auto-reply acknowledgment of each candidate application.
In conclusion, recruitment is a two-way street. Candidates want to find the perfect company, and companies want to find the perfect candidates. Integrating AI in the recruitment process has the potential to benefit both parties and help solve challenges – like the time is takes to hire, finding the right hire, and ensuring overall satisfaction for the applicant and the business seeking candidates.
Canadian Government Grants to Develop Your Workforce
Hiring the right employees can help your business reach new heights. Regardless of the scale of your hiring efforts, increasing your workforce can lead to significant benefits to an organization’s profitability. If you are planning on expanding or developing your workforce, there are Canadian government grants for small business recruitment activities to ensure they have qualified workers with the necessary skills and capabilities to support ongoing and future business plans.
A wide range of wage subsidies are available to Canadian businesses, covering a percentage of the new employee’s hourly wage rate or a set hourly subsidy dollar amount. Reach out to Ryan via the Wage Subsidy Identifier and we can identify hiring grants available for your business.
| 2019-04-04T00:00:00 |
2019/04/04
|
https://funding.ryan.com/blog/business-strategy/artificial-intelligence-recruitment-process/
|
[
{
"date": "2019/04/04",
"position": 88,
"query": "artificial intelligence hiring"
}
] |
How Americans see automation and the workplace in 7 charts
|
How Americans see automation and the workplace in 7 charts
|
https://www.pewresearch.org
|
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Around half of U.S. adults (48%) say job automation through new technology in the workplace has mostly hurt American workers, while just 22% say ...
|
Automation already plays a significant role in the U.S. workplace, and most Americans expect technological advances to continue to alter the job landscape in the decades ahead. These seven charts, based on recent Pew Research Center surveys, highlight Americans’ views toward job automation:
Most Americans anticipate widespread job automation in the coming decades. About eight-in-ten U.S. adults (82%) say that by 2050, robots and computers will definitely or probably do much of the work currently done by humans, according to a December 2018 Pew Research Center survey. A smaller share of employed adults (37%) say robots or computers will do the type of work they do by 2050.
The U.S. public generally anticipates more negative than positive effects from widespread job automation. Around three-quarters of Americans (76%) say inequality between the rich and the poor would increase if robots and computers perform most of the jobs currently being done by humans by 2050. Only a third (33%) believe it’s likely that this kind of widespread automation would create many new, better-paying jobs for humans.
In a May 2017 Pew Research Center survey, around four-in-ten U.S. adults said an automated future would make the economy more efficient, let people focus on the most fulfilling aspects of their jobs or allow them to focus less on work and more on what really matters to them in life. In each instance, a majority of the public said these positive outcomes are unlikely.
When it comes to workplace automation that has already occurred, Americans are more likely to say it has hurt U.S. workers than helped them. Around half of U.S. adults (48%) say job automation through new technology in the workplace has mostly hurt American workers, while just 22% say it has generally helped, according to the 2018 survey. About three-in-ten (28%) say these advances have neither helped nor hurt U.S. workers.
Adults 50 and older are more likely than younger Americans to say job automation has hurt workers (55% vs. 43%), as are adults with a high school diploma or less when compared with those with a bachelor’s degree or more (53% vs. 42%).
Americans think automation will likely disrupt a number of professions – but they are less likely to foresee an impact on their own jobs. In the Center’s 2017 survey, around three-quarters of U.S. adults (77%) said it was very or somewhat likely that fast food workers would be replaced by robots or computers in their lifetimes, while about two-thirds (65%) said the same about insurance claims processors. Around half said automation would replace the jobs of software engineers and legal clerks, while smaller shares said it would affect construction workers, teachers or nurses. Three-in-ten Americans said their own jobs would become automated in their lifetimes. (A slightly different question was asked in the 2018 survey.)
Young adults and part-time workers are especially likely to have been personally affected by workforce automation. In 2017, 13% of those ages 18 to 24 had either lost a job or had pay or hours reduced because their employers replaced their positions with a machine, robot or computer program. That compares with slightly smaller shares of those ages 30 and older. Those employed part time were also slightly more likely than those employed full time (11% vs. 5%) to cite these personal impacts from automation.
Many Americans say there should be limits on job automation – and majorities support certain policies aimed at doing so. Nearly six-in-ten Americans said in 2017 that there should be limits on the number of jobs that businesses can replace with machines, even if those machines are better and cheaper.
Most Americans also expressed support for policies aimed at limiting automation to certain jobs or cushioning its economic impact. A large majority (85%) said they would support restricting workforce automation to jobs that are dangerous or unhealthy for humans to do. Six-in-ten said they would favor a federal policy that would provide a guaranteed income for all citizens to meet basic needs in the instance of widespread job automation, and a similar share (58%) said they would support a federal program that would pay people to do tasks even if machines are able to do the work faster and more cheaply.
Americans are divided over whose responsibility it is to take care of displaced workers in the event of far-reaching job automation. Half of U.S. adults said that in the event that robots and computers are capable of doing many human jobs, it is the government’s obligation to take care of displaced workers, even if it means raising taxes substantially, according to the 2017 survey. A nearly identical share (49%) said that obligation should fall on the individual, even if machines have already taken many human jobs.
Democrats and Democratic-leaning independents were far more likely than Republicans and GOP leaners (65% vs. 34%) to say the government is obligated to help displaced workers in the event that robots become capable of doing many human jobs, while Republicans were much more likely to say individuals should be responsible (68% vs. 30% of Democrats).
| 2019-04-08T00:00:00 |
2019/04/08
|
https://www.pewresearch.org/short-reads/2019/04/08/how-americans-see-automation-and-the-workplace-in-7-charts/
|
[
{
"date": "2019/04/08",
"position": 33,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 33,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 33,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 33,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 30,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 32,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 32,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 31,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 33,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 67,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 29,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 32,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 33,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 33,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 30,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 28,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 33,
"query": "job automation statistics"
},
{
"date": "2019/04/08",
"position": 32,
"query": "job automation statistics"
}
] |
The Government Workforce Says It's Looking Forward to Working ...
|
The Government Workforce Says It’s Looking Forward to Working With AI » Community
|
https://www.govloop.com
|
[] |
While there is a significant appetite for continued education around the opportunities that intelligent technologies like AI present to federal ...
|
In a recent survey, government employees said they were looking forward to learning more about working with AI. The Government Business Council survey, sponsored by our company, showed that respondents were receptive to learning about and using new intelligent technologies such as artificial intelligence (AI) and they welcome opportunities to build their skill sets.
It’s great news that employees feel the excitement and want to dive in and learn more. However, we believe agencies need to think about how they approach integrating the workforce and AI earlier in the process and bring them along on the journey. Here’s how it might be done:
Co-create the vision
By putting together an AI approach that includes the workforce, people become part of envisioning and ultimately adopting the solution. Sometimes employees see technology acquired and “bolted on” to fix a problem, and they don’t understand the big picture or what that technology is trying to solve.
Questions that often arise are: will it make my job easier? Will it help me focus on other higher value parts of my work? By having a multi-year AI vision that is co-created with employees who best understand the work, the technology is more likely to gain acceptance, and employees better understand how it will impact or amplify their work and role. As an added bonus, you’ll create awareness among the workforce and identify some early adopters who are involved from the beginning.
Understand the work
AI adoption should be pursued in the context of shifting employees from low-value to high-value work. Teams that are augmented or given the opportunity to work with intelligent technologies are likely going to become the norm. Employees are the contextual experts and can define the tasks that best lend themselves as AI candidates. By using a design-led approach, employees can identify the tasks AI can do, and that will help them do their jobs more efficiently and also reinforce their trust in the solution.
Don’t underestimate the role of change management and training
While there is a significant appetite for continued education around the opportunities that intelligent technologies like AI present to federal employees, the workforce also believes that AI will benefit them individually in their roles. Our survey shows that federal workers understand that AI will create opportunities for them to continue to grow their skills and develop their career. Contrary to that, the survey also found that 73 percent of respondents said their agency’s leaders could do more to communicate a long-term vision for what AI will mean for their workplace. Agencies should take the opportunity now to address this disconnect before the void is filled with assumptions that are not grounded in fact.
Enter: Change management and training. Employees need to know what to expect when it comes to AI and their job, their team and their work. Communication should begin early and happen often. Employees also want training on AI but are unsure of what that really means. They want to be knowledgeable about what AI will do, what the technologies are and the new ways of working with it, thereby identifying how they individually will interact with it.
The answers could come through ongoing educational bite-size learning versus a longer 3-4 day AI skills training course. Softer skills training allows the employee to see how work will be changing as AI is introduced, what skills will become important to hone when interacting with AI, and how can they learn about the opportunities being explored at their Agency. Take the time to develop a communications plan, messaging, and governance structure that creates an informed workforce around AI.
Bottom line: AI is coming, and it’s time to put the employee at the center of the planning.
Co-authored by Kristen Vaughan, managing director and Human Capital Practice lead and Britaini Carroll, Human Capital Practice workforce lead.
Dominic Delmolino is a GovLoop Featured Contributor. He is the Chief Technology Officer at Accenture Federal Services and leads the development of Accenture federal’s technology strategy. He has been instrumental in establishing Accenture’s federal activities in the open source space and has played a key role in the business by fostering and facilitating federal communities of practice for cloud, DevOps, artificial intelligence and blockchain. You can read his posts here.
| 2019-04-23T00:00:00 |
https://www.govloop.com/community/blog/the-government-workforce-says-its-looking-forward-to-working-with-ai/
|
[
{
"date": "2019/04/23",
"position": 80,
"query": "government AI workforce policy"
},
{
"date": "2019/04/23",
"position": 93,
"query": "government AI workforce policy"
}
] |
|
The Legal and Ethical Implications of Using AI in Hiring
|
The Legal and Ethical Implications of Using AI in Hiring
|
https://hbr.org
|
[
"Ben Dattner",
"Tomas Chamorro-Premuzic",
"Richard Buchband",
"Lucinda Schettler",
"Is An Executive Coach",
"Organizational Development Consultant",
"The Founder Of New York City Based",
"Is The Chief Innovation Officer At Manpowergroup",
"A Professor Of Business Psychology At University College London",
"At Columbia University"
] |
Many of these technologies promise to help organizations improve their ability to find the right person for the right job, and screen out the wrong people for ...
|
Digital innovations and advances in AI have produced a range of novel talent identification and assessment tools. Many of these technologies promise to help organizations improve their ability to find the right person for the right job, and screen out the wrong people for the wrong jobs, faster and cheaper than ever before.
| 2019-04-25T00:00:00 |
2019/04/25
|
https://hbr.org/2019/04/the-legal-and-ethical-implications-of-using-ai-in-hiring
|
[
{
"date": "2019/04/25",
"position": 80,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 80,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 68,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 58,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 75,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 58,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 57,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 60,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 61,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 68,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 69,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 71,
"query": "AI hiring"
},
{
"date": "2019/04/25",
"position": 77,
"query": "AI hiring"
}
] |
How Will Machine Learning Transform the Labor Market?
|
How Will Machine Learning Transform the Labor Market?
|
https://www.hoover.org
|
[
"Research Team"
] |
Artificial intelligence (AI) has begun to transform the economy as it as enables machines to do more and more of the cognitive tasks that were ...
|
Energy & Environment
Introduction
The twenty-first century will be the century of intelligent machines. Artificial intelligence (AI) has begun to transform the economy as it as enables machines to do more and more of the cognitive tasks that were once done only by humans. In the coming decade, many existing tasks will be replaced by machines, while new ones will emerge. Almost every job will be affected in some way and most will need to be redesigned. Businesses will rise and fall depending on how well they understand, foster and harness the changing skills that are needed to be productive. Economies will thrive if they can create and update the institutions needed to create these skills.
In particular, the branch of AI known as machine learning (ML) has advanced significantly in just the past decade, largely reflecting improvements in the area of deep learning, a technique that trains large neural networks on large datasets (Brynjolfsson and Mitchell 2017). Three different types of advances, each of about two orders-of-magnitude, have combined to make this possible: 1) an increase in the quantity and quality of digital data, 2) improvements in computational power, reflecting not only the march of Moore’s Law, but also new specialized architectures like GPUs and TPUs, and 3) improved algorithms (McAfee and Brynjolfsson 2017). As a result, the performance of ML algorithms has improved significantly. In a highly cited example, the image recognition algorithms on the ImageNet Dataset improved from barely 70% in 2010 to over 97% today, and now surpass human level performance on the same data. Voice recognition and natural language processing, machine translation, recommendation systems, gaming and many other tasks have also seen striking improvements (Shoham et al. 2018). Because capabilities like vision, speech and decision-making are so fundamental for most occupations, these improvements to technology suggest that substantial changes in the nature of work can be expected.
Despite these impressive advances, however, ML is far from being capable of doing the full range of human cognitive tasks. This raises some obvious questions. What tasks can ML do well, and what tasks are best done by humans? What are the implications for jobs, industries and different geographies? How can we quantify the changing value of human skills for businesses? In this paper, we seek to address these questions by drawing on several streams of research that have been underway for several years. First, we report on work based on interviews with a set of leading experts in machine learning to develop a set of criteria, or a rubric, for distinguishing which tasks are most suitable for machine learning (Brynjolfsson and Mitchell 2017; Brynjolfsson, Mitchell, and Rock 2019). In turn, we applied this rubric to score 18,112 tasks in 950 occupations spanning most of the U.S. economy to create a guide to how different occupations, industries and regions would likely be affected as the use of ML becomes more pervasive (Brynjolfsson, Mitchell, and Rock 2019). Second, to illustrate how different types of human capital, including skills and education, affect firm value, we draw on ongoing work using data from LinkedIn, Compustat, and other sources to newly quantify these relationships.
Our first set of findings are that while existing ML technologies are not able to automate all the tasks that comprise any of the occupations we study, they are sufficiently advanced to do at least some tasks in almost every job. This suggests substantial redesign of work and significant reskilling will be needed to harness the potential of ML. Our findings suggest that people in lower wage jobs will be disproportionately affected as will those in retailing and transportation industries. People in smaller cities are also more likely to be affected than larger ones.
Investments into the redesign of work can yield significant value for firms. In a second set of findings, drawing on data from LinkedIn, we find that skills and education have value not only to the employees who acquire them, but also to the owners of the companies where those employees work. In fact, the value of IT-related investments has grown dramatically in recent years and, based on a sample of publicly traded firms, as of the end of 2016 amounts to about 39% of the value of installed property, plant, and equipment (about $8-9 billion in ITIC per firm in the sample). This implies that firms have a large incentive to invest in creating and updating the skill sets needed to take advantage of emerging IT—most recently, ML—as these technologies become increasingly pervasive. Although many skills will be affected, the skills needed to implement ML are a notable special case: their value has grown markedly as new technologies like Tensorflow have boosted their economic impact (Rock 2019a). The magnitude and scope of the reskilling and business process redesign needed to put ML breakthroughs into practice means that it will require years if not decades before the full effects are felt, just as with earlier technological breakthroughs. (Brynjolfsson, Rock, and Syverson 2018).
The Labor Market’s Exposure to Machine Learning Technology
We apply a task-level approach to understanding the effects of ML. This is the most natural unit of analysis for specific capabilities. Detailed information about task-level exposure to ML can then be aggregated to improve our understanding of its effects on many aspects of the economy, including occupations, firms, industries, and regions. In particular, occupations can be considered useful bundles of tasks assigned to similar types of workers. The task-level approach relates worker labor inputs to new types of technological capital within a production function (Autor, Levy, and Murnane 2003; Acemoglu and Autor 2011). Inherent in the managerial decision to replace human tasks with capital services is a trade-off between wages paid to workers and capital rental costs for the machines that could do the same tasks. Increasing machine capabilities or decreasing capital costs for a given task increases incentives to substitute capital for labor in this class of models.
While they are typically stable in the short-run, the set of tasks within each occupation changes over time, as does the nature of many of the tasks themselves. New tasks are created and the value of old tasks changes, altering what the most productive mixture of tasks for a given occupation might be. A related class of models (Acemoglu and Restrepo 2018) elucidates the trade-off between investment in automation technologies and investment in creating new tasks. In these models, increased automation increases the returns to innovative activity in creating new tasks for human labor. In nearly all cases though, the impact of new technology on labor demand is contingent on more than simple human labor task replacement potential.
Making predictions about the impact of ML on labor demand is challenging because any given occupation most often performs a wide variety of tasks. Inevitably, some of those tasks are more suitable for machine learning than others. Focusing on what ML can do with respect to the tasks currently done by workers, however, can yield insight into which tasks are most exposed to technology. Brynjolfsson and Mitchell (2017), Brynjolfsson, Mitchell, and Rock (2018, 2019), and (Brynjolfsson et al. 2019) build, refine and extend a rubric that seeks to identify the tasks in the economy which have the greatest Suitability for Machine Learning (SML). The rubric consists of 23 evaluative questions with potential answers ranging from 1 (very low SML) to 5 (very high SML). The rubric is used to create a score for 2,059 detailed work activities from the U.S. government’s O*NET database. In turn, these scores were aggregated into 950 occupations consisting of 18,112 tasks which share detailed work activities across occupations. After being validated by experts in machine learning and assessed by a team at MIT on a representative set of tasks, rubric evaluation was scaled up to the full set of tasks by respondents on CrowdFlower, as described in Brynjolfsson, Mitchell, and Rock (2018). Subsequent iterations in Brynjolfsson, Mitchell, and Rock (2019) and Brynjolfsson et al. (2019) use data from Amazon Mechanical Turk respondents with some further refinements and improved quality control. The questions are designed such that a “1” (Strongly Disagree) corresponds to low SML and a “5” (Strongly Agree) corresponds to high SML, and neutral exposure corresponds to a score of 3 (Neither Agree nor Disagree). In most of the analyses, the values for each of these individual scores are essentially averaged to get an overall task-weighted occupation-level SML score.1
Exposure to ML does not necessarily mean that the human labor will be replaced or even reduced in that occupation. As discussed in Brynjolfsson and Mitchell (2017), in addition to substitution, ML can also be used in at least five other ways: to complement labor, to increase demand for it by lowering costs, to change demand by changing overall income, to change information flows and thus information asymmetries, or to drive a reorganization of work. While there has been much emphasis on the first of these possibilities (automation and thus substitution) research suggest that the biggest effect in the coming years will be in driving a redesign of work, as only some tasks in most occupations are suitable for machine learning, while others will continue to require human labor.
Occupations vary considerably in their exposure to machine learning as measured by SML score. Figure 1 below shows the distribution of SML across jobs, tasks, and activities. Very few, if any, occupations are completely exposed to ML. The maximum SML value of any task is a 4.0, with a minimum of 2.13 across all tasks. Strikingly, nearly all occupations have at least one task with a relatively high SML score. Figure 2 shows the count of occupations (vertical axis) against their proportions of task SML above the 90th and 50th percentile of SML (horizontal axis). No occupation has tasks entirely in the 90th (or higher) SML percentile, but most occupations have at least some tasks above this threshold, and almost all jobs have some number of tasks above the 50% threshold.
If ML could do all tasks in a particular occupation, there would be little need or opportunity to re-organize the tasks in that job. It would be fully automated. Likewise if there were nothing ML could do in that occupation, there would be no reason to re-organize the occupation to unlock the gains from ML technology. The fact that most occupations fall between these extremes underscores the likelihood that machine learning will drive re-organization and re-engineering of how tasks are bundled and assigned into occupations. Indeed, Brynjolfsson et al. (2019), highlight that re-organization of work, not automation or substitution, is the labor demand force with the greatest economic potential for ML (see Figure 1 and 2).2, 3
The occupational impact of ML will be shared across many different types of labor, but it will also be uneven. Some occupations, and therefore industries and regions as well, are more exposed to ML than others. Figure 3 shows that lower wage occupations have relatively higher SML scores, though all wages levels have some occupations at either end of the SML spectrum.4 Figures 4A and 4B shows the standardized SML scores and Image Data scores (respectively) aggregated by occupation type to the region level. Large cities tend to have lower relative overall SML scores. In contrast, much of the potential for using ML for image analysis value is more concentrated in large cities. Finally, Figure 5 shows that employment-weighted SML by industry. Accommodation and Food Services, Transportation and Warehousing, and Retail Trade are relatively more exposed to the re-organization impact of ML than Education and Health Care (see Figures 3-5).5, 6, 7
Higher SML tasks are often clerical tasks like balancing accounts or medical transcription or the type of routine work that might be done in a factory (e.g. inspecting items for defects). Most occupations have some component task that does something clerical. But taking advantage of this new technology will require adjustments to how these tasks are performed together. That means changing occupations, but also business processes.
Firms and organizations will have to build new kinds of intangible capital to complement the new types of technological capital created by machine learning advances. At the same time, know-how and tacit knowledge built for the old economic environment will lose value (Greenwood and Yorukoglu 1997). In this way, the coming changes from ML technologies are similar to many earlier varieties of information technology. Part of what is happening now is an extension of the recent past, which has been characterized by waves of investment in networked computing, databases, and other information technologies.
IT, Intangible Capital, and Value
The business process reengineering needed to unlock value from emerging technologies, such as ML technologies, can comprise a growing category of a firm’s assets (R. E. Hall 2001; Brynjolfsson and Hitt 2000; Bresnahan, Brynjolfsson, and Hitt 2002). This “IT-related intangible capital” (“ITIC”) is the result of investments that firms make into becoming information processing organizations, including investments in business process reengineering that facilitate rapid information acquisition, employee learning, and decision-making. These types of assets are likely to play an increasingly important role in explaining economic outcomes such as growth and firm performance. In some ways, these investments are similar to those that firms make in physical capital such as trucks or manufacturing equipment. Just as units of physical capital enable the conversion of raw materials to goods, units of ITIC enable firms to convert information and ideas into products and services.
The biggest difference is that the measurement of these IT-based intangible assets has proven elusive. Investment into this form of capital is largely invisible (to researchers) and depreciation rates are idiosyncratic and variable, so conventional methods for measuring capital stocks cannot be easily applied (B. H. Hall 1993). For other types of assets, one might use market transactions, such as a lease or resale price, to derive prices, but there are no observable markets for trading stand-alone ITIC—firms cannot sell their management practices or learning cultures.
As an alternative approach to measuring these assets, we can use methods from the literature on intangible assets to derive changes in the prices and quantities of ITIC in U.S. firms. This literature argues that the quantities of a firm’s intangible assets can be inferred from the value of its securities (R. E. Hall 2001). The intuition behind this approach is that under reasonable conditions, the value of a firm’s securities is equivalent to the value of its capital assets, which is in turn equal to the price of installed capital times its quantity, or equivalently, the ratio of market value to installed capital is equal to its price (equation 1).
Because firms’ investments in these assets are governed by an adjustment cost condition (equation 2), from the marginal adjustment cost function8 evaluated at the investment rate at time t:
Thus, we have two equations with two unknowns which can be solved to recover quantities of tangible or intangible capital. In other words, even when investment into capital cannot directly be observed, its quantities can be computed using changes in the value of a firm’s assets. In the case of IT, the value of a firm’s ITIC is not recorded, but these ITIC values can be inferred using proxy measures of investment into a correlated input, such as IT infrastructure (Brynjolfsson, Hitt, and Yang 2002). We use the method described above to derive quantities of ITIC.
A challenge with this approach is that a lengthy series of firm-level IT investment data are needed, and such data have historically been difficult to obtain at the firm-level. To address this problem, we generated an IT series of one of the most important inputs into the construction of ITIC—IT labor—to enable the application of the methods described above.
The data were obtained from LinkedIn, a leading online professional network web site upon which individuals post their employment histories, including information for each job they have held on employer, job title, and years spent at the firm. Employer data generally include name, size, and industry. We use the employment histories of the workers on LinkedIn who identify themselves as IT workers in order to measure the distribution of IT employment in large public firms over a period of approximately three decades.9
The length of this labor series is important for two reasons. First, it is consistently available through the period of the dot-com bust, after the crash, and through the last decade including capturing the rise in spending around big data, data science, and machine learning, so we can test how much of the value of IT-related intangibles in the late 1990’s was attributable to investor mispricing or when spending on this asset resumed. Second, in the absence of direct measures of spending on IT-related infrastructure, the wages paid to IT labor are among the principal inputs into the construction of ITIC. In other words, firms can build new information structures around old IT capital, so IT wages may be a more relevant input to ITIC investment than IT capital spending.
The key results from the application of these IT labor data to the model described earlier are shown in Figure 6 below. First, by 2016, the stock of IT-related intangible capital in our panel of firms10 had grown to about 25% of the value of physical capital stock. Despite swings in the value of ITIC around the dot-com boom and bust, firms continued to accumulate IT-related intangible capital well after the bust and through the 2008 recession (see Figure 6).
We also find evidence of significant heterogeneity in the distribution of these IT intangible assets within the U.S. economy. Figure 7 plots quantities of ITIC by quintile in terms of ITIC stock value. This figure indicates that growth in this form of capital, far from being evenly distributed, has been concentrated in a relatively small set of firms, which is consistent with other patterns of digitization and firm dominance that have been observed in the recent economic data. Higher quantities of ITIC for these firms suggests that they will enjoy significant production-based advantages in the future.
The Market Value of Skills Used for Implementing Machine Learning
What does our approach to assessing ITIC say about skills that can be used to implement machine learning? The types of intangible assets that firms build change over time to match the IT environment. Changes in technology alter the value of the installed capital base as well, including the value of human capital. Using the LinkedIn skills data, Rock (2019a) finds that Google’s open source launch of TensorFlow lifted the market value of AI-using publicly traded firms by 4 to 7%. TensorFlow is a software package that makes it easier for software engineers and data scientists to implement deep learning models. Similar to the way that coding in Python or C++ is easier than coding in assembly language, TensorFlow saves a lot of the effort required to build and train neural networks. Because of that, companies that had intangible capital related to AI increased in value when TensorFlow was launched. The complementary workers these firms needed to generate value from their intangible assets became more abundant. While impactful to the companies involved, this is one relatively small example of the overall rising tide in IT-related intangible assets. Realizing the returns to AI skill investments often demands extensive investment in other IT skills like cloud computing, data engineering, and specialized management. All of these skill varieties require complementary investments in IT-related intangible assets as well (Tambe 2014).
Implications of the growth of ITIC
Our findings suggest that investment in information structures and related skills produces relatively long-lived, durable assets. For policy makers, these findings suggest that the large waves of investment in IT-related intangibles are associated with the development of significant productive capacity and, all else being equal, that this should boost prospects for long-run growth. The fact that ITIC assets behave similarly to other capital assets in recent years is itself interesting. This may be because translating organizational innovations into productive capital requires significant investment in reengineering and skills.
There are, however, important differences between ITIC and physical capital. Unlike most types of physical capital, ITIC has diminished value outside the context of the firm. This has important implications for firm valuation and acquisition. Development researchers have traditionally looked at capital accumulation as an engine for growth. The lack of secondary markets for IT-related intangible assets ties these questions together in an important way to firm health. When firms are dismantled, ITIC is likely to disappear. Therefore, it is worth continuing to further our understanding how the rising importance of ITIC, in conjunction with notable differences between ITIC and physical assets, impacts economic behavior.
Conclusion
Since the industrial revolution, general purpose technologies like the steam engine and electricity have driven economic growth and higher living standards. We believe that the most important general purpose technology of our era is AI, especially ML. A key feature of general purpose technologies is that they enable complementary innovations and investments. Among the most important complements are new skills and new business processes. Unlike plant and equipment, skills and process innovations are intangible. This makes it difficult to measure and makes it subject to underinvestment and malinvestment. By using the SML framework, we can identify some of the tasks that are most likely to be affected by machine learning, as well as some of their associated skills.
The large increase in IT-related intangible capital in general, coupled with the surge in the value of skills needed for implementing ML in particular, suggest an important role for businesses reskilling the workforce. Our analysis shows that shareholders stand to benefit from when the employees of their firms have more of the right sets of skills. However, while the advances in technology have often been breathtaking, the reskilling of the workforce and the reinvention of business processes has lagged. This is reflected, for instance, in declining business dynamism according to work by Decker et al. (2016). The key bottleneck for unlocking value often is not technology but people. Therefore, for adapting to ML enabled work, the grand challenge of the 21st century will be speeding the adoption of new skills and organizational practices that support these technologies.
Erik Brynjolfsson is the director of MIT’s Initiative on the Digital Economy and professor of management science at the MIT Sloan School. Daniel Rock is a doctoral candidate at MIT Sloan, and Prasanna Tambe is an associate professor of operations, information, and decisions at the University of Pennsylvania’s Wharton School.
Supporting Data
Figure 1. Distribution of Counts of Suitability for Machine Learning (SML) Score for Occupations, Tasks, and Detailed Work Activities
Figure 2. Histogram of Occupations by Proportion of Tasks with SML Larger than 90th and 50th Percentile Thresholds
Source for Figures 1 and 2: Brynjolfsson, Mitchell, and Rock (2018a); Rock (2019)
Figure 3. SML Score vs. 2016 Median Wage Percentile; Regression Coefficient: -0.0034 (t-stat = 18.5)
Figure 4A. Standardized SML Score by Region
Source for Figure 3: Brynjolfsson, Mitchell, and Rock (2018a); Rock (2019)
Figure 4B. Standardized Image Data Score by Region
Figure 5. Employment-Weighted Average SML by 2-Digit NAICS Industry
Source for Figures 3-5: Brynjolfsson, Mitchell, and Rock (2018a); Rock (2019)
Figure 6. Change in Quantities of ITIC and PPE for Publicly Traded Firms from 1987 to 2016
Figure 7. Quantities of ITIC by Firm Quartile
Acknowledgements
This work was done in conjunction with the MIT Work of the Future Task Force. We received access to LinkedIn data as participants in the LinkedIn Economic Graph Challenge. We thank the MIT Initiative on the Digital Economy for generous funding.
Bibliography
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Acemoglu, Daron, and Pascual Restrepo. 2018. “The Race between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment.” American Economic Review.
Autor, David, Frank Levy, and Richard J. Murnane. 2003. “The Skill Content of Recent Technological Change: An Empirical Exploration.” The Quarterly Journal of Economics 118 (4):1279–1333.
Bresnahan, Timothy F, Erik Brynjolfsson, and Lorin M Hitt. 2002. “Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence.” The Quarterly Journal of Economics 117 (1). MIT Press:339–76.
Brynjolfsson, Erik, Morgan R. Frank, Tom Mitchell, Iyad Rahwan, and Daniel Rock. 2019. “Machine Learning, Labor Demand, and the Reorganization of Work.”
Brynjolfsson, Erik, Lorin M. Hitt, and Shinkyu Yang. 2002. “Intangible Assets: Computers and Organizational Capital.” Brookings Papers on Economic Activity 2002 (1):137–98.
Brynjolfsson, Erik, and Lorin M Hitt. 2000. “Beyond Computation: Information Technology, Organizational Transformation and Business Performance.” Journal of Economic Perspectives 14 (4):23–48.
Brynjolfsson, Erik, and Tom Mitchell. 2017. “What Can Machine Learning Do? Workforce Implications.” Science 358 (6370):1530–34.
Brynjolfsson, Erik, Tom Mitchell, and Daniel Rock. 2018a. “Machine Learning and Occupational Change.” Unpublished Working Paper. MIT.
———. 2018b. “What Can Machines Learn, and What Does It Mean for Occupations and the Economy?” AEA Papers and Proceedings, 43–47.
Brynjolfsson, Erik, Daniel Rock, and Chad Syverson. 2018. “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics.” In Economics of Artificial Intelligence. University of Chicago Press.
Decker, Ryan A, John Haltiwanger, Ron S Jarmin, and Javier Miranda. 2016. “Declining Business Dynamism: What We Know and the Way Forward.” American Economic Review 106 (5):203–7.
Greenwood, Jeremy, and Mehmet Yorukoglu. 1997. “1974.” In Carnegie-Rochester Conference Series on Public Policy, 46:49–95.
Hall, Bronwyn H. 1993. “The Stock Market’s Valuation of R&D Investment during the 1980’s.” The American Economic Review 83 (2):259–64.
Hall, Robert E. 2001. “The Stock Market and Capital Accumulation.” The American Economic Review 91 (5):1185–1202.
McAfee, Andrew, and Erik Brynjolfsson. 2017. Machine, Platform, Crowd : Harnessing Our Digital Future. Harnessing Our Digital Future.
Rock, Daniel. 2019a. “Engineering Value: The Returns to Technological Talent and Investments in Artificial Intelligence.”
———. 2019b. “Essays on Information Technology, Intangible Capital, and the Economics of Artificial Intelligence.” Massachusetts Institute of Technology.
Shoham, Yoav, Raymond Perrault, Eric Brynjolfsson, Jack Clark, James Manyika, Juan Carlos Niebles, Terah Lyons, John Etchemendy, and Z Bauer. 2018. “The AI Index 2018 Annual Report.” Stanford.
Tambe, Prasanna. 2014. “Big Data Investment, Skills, and Firm Value.” Management Science 60 (6):1452–69.
| 2019-05-06T00:00:00 |
https://www.hoover.org/research/how-will-machine-learning-transform-labor-market
|
[
{
"date": "2019/05/06",
"position": 99,
"query": "AI labor market trends"
}
] |
|
AI-assisted recruitment is biased. Here's how to make it more fair
|
AI-assisted recruitment is biased. Here’s how to make it more fair
|
https://www.weforum.org
|
[] |
Understanding at which points algorithms come into play in the hiring process can help identify the origins of bias. Typically, hiring is not a ...
|
Chances are that you have sent hundreds or even thousands of resumes and cover letters over the years to potential employers. This observation is supported by the fact that according to Bureau of Labor Statistics, wage and salary workers in the US have been with their current employer for an average of 4.2 years. Younger workers, however - those aged between 25 and 34 - have on average been with their current employer for just 2.8 years, which suggests they are part of an increasingly transient workforce that is more accustomed to applying for jobs.
This pattern can also be observed in other countries - with a few notable exceptions, such as Japan. As a result, many millennials from around the world are growing increasingly accustomed to this new career model of sending out a near-constant barrage of cover letters and CVs.
Eventually, these applications may lead to you accepting a job offer that determines your quality of life – your income, the time you can spend with friends and family, and the neighbourhood you live in.
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In some cases, however, the chances of getting the job for which you have applied for are systematically biased. For example, it has been shown that in the US labour market, African-American names are systematically discriminated against, while white names receive more callbacks for interviews. However, we observe bias not only because of human error, but also because the algorithms increasingly used by recruiters are not neutral; rather, they reproduce the same human errors they are supposed to eliminate. For example, the algorithm that Amazon employed between 2014 and 2017 to screen job applicants reportedly penalised words such as ‘women’ or the names of women’s colleges on applicants’ CVs. Similarly, researchers from Northeastern University, the University of California and Upturn, a public-interest advocacy group, have demonstrated that Facebook’s housing and employment ads delivery follows gender and race stereotypes.
What can you do about it?
Given these biases, what steps can you take to maximise the chances that your CV and cover letter will land you an interview?
Today, recruiters in large companies such as Target, Hilton, Cisco, PepsiCo and Amazon use predictive hiring tools to both reduce the time and cost - and to hypothetically increase the quality and tenure - of each new hire. Understanding at which points algorithms come into play in the hiring process can help identify the origins of bias.
Typically, hiring is not a single decision, but a process involving many small decisions that culminate in a job offer. The aim of the first step - known as sourcing - is to generate a strong set of applicants (see figure 1). This can be done via advertisements, active headhunting or attractive job descriptions. Usually, artificial intelligence (AI) is used to optimise the display of job ads as well as their wording, as done by companies who provide ‘augmented writing’, such as Textio.
The second step, screening, is crucial as this is where algorithmic bias can strongly influence whether your application is rejected. Screening uses algorithms that systematically decipher your cover letter and CV and save this information in the company’s HR database. This information could include your years of experience, the languages you speak, the university degrees you obtained and the countries in which you have you worked. Algorithms are then used to narrow down the selection of candidates automatically – not in an affirmative way, but by rejecting those who do not fit. The company CVViZ, for example, employs machine learning algorithms to screen resumes for keywords in context and to create relative rankings between the different candidates.
Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias Image: Upturn
If you have made it through the screening process, you may be invited to an interview that might also use different algorithms to support the employer’s final selection decision. HireVue, a US-based company, assesses candidates based on the keywords, facial expressions and tones they use in video interviews. After a video interview you may get a face-to-face interview, after which you are rewarded with an offer.
The use of machine learning algorithms in each of these steps can lead us to question the fairness of an AI-driven recruitment system. As in the case of Facebook, mentioned above, bias may be present in how job advertisements target potential employees. In other cases, web crawlers try to find matching candidates to job descriptions by scanning information from publicly available online sources - and while in this case one might argue the unfairness is limited because it doesn’t prevent you from applying, this screening process may already display strong bias that is difficult to overcome.
Algorithms are often trained to read specific formats of CVs and resumes, which could mean your CV is not evaluated properly. For example, in Japan there is a common CV template (Rirekisho) used by all job applicants. In China, applicants list their work experience in reverse chronology. Other cultural differences exist between American CVs and European CVs; the former is usually one page long with no photo, while the latter can be between two to three pages, headlined with a photo.
If your CV has been successfully parsed - that is, translated into machine-readable data - another algorithm will rank your application vis-à-vis other applications based on the data in your CV and your cover letter. Each factor, such as your years of experience, languages, software skills and the set of words you use, to name but a few success metrics, will be weighted according to what is estimated to have successfully worked in the past. Past hiring decisions are used to train the algorithm to evaluate who is most likely to be the ‘right’ applicant. Often this approach inherently replicates the same biases that were present before the arrival of AI recruiting tools. If the gender distribution of the training data was strongly imbalanced, this may be replicated by an algorithm even if gender is not included in the information provided in the application documents. For example - as in Amazon’s case - strong gender imbalances could correlate with the type of study undertaken. These training data biases might also arise due to bad data quality or very small, non-diverse data sets, which may be the case for companies that do not operate globally and are searching for niche candidates.
Similarly, the evaluation of video interviews conducted prior to any in-person interaction may replicate biases that rely on training data if it has not been thoroughly vetted against categories such as gender, age or religion.
Recommendations
There are several steps both job applicants and employers can take to maximise the chance that the right application will be read by a human being making the ultimate hiring call.
As an applicant, you should:
1) Make sure your CV is formatted according to local norms. Evaluate which length, layout, photo and format are most appropriate. Avoid graphics and fancy fonts that may not be readable by the algorithm.
2) Elaborate on your work experience and adapt your language to that of the job description.
3) Make sure to include key information on your CV – what is not on your CV cannot be evaluated. For example, mention the month and year for each position you held instead of only the year.
4) Optimise your online brand by using the appropriate jargon. Use language that speaks to the job family you are interested in. For example, IT jobs have different titles such as ‘full stack developer’ that are often used in connection with programming languages such as C++ or PHP.
As an employer using machine learning algorithms in the hiring process, ensuring fairness is key. The following concepts, taken from recent research carried out at Delft University of Technology, may provide a guide:
Justification: Does it make sense for an organization of a certain size with specific hiring needs to employ AI hiring tools, given the data requirements and the need for bias remediation?
Explanation: Does the AI tool explain its decisions and are those explanations made available to the recruiter and the applicant? If algorithmic information is proprietary, are counterfactual explanations taken into consideration?
Anticipation: Are mechanisms in place to report biased decisions and what are the recourse mechanisms in place?
Reflexiveness: Is the organization aware of its changing values and its reflection in the data it uses? How is data collected and which limitations become evident?
Inclusion: Do you think about diversity in your team and in the evaluation results?
| 2019-05-09T00:00:00 |
https://www.weforum.org/stories/2019/05/ai-assisted-recruitment-is-biased-heres-how-to-beat-it/
|
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|
Future of Work: The Impact of AI on the Future Workplace
|
Future of Work: The Impact of AI on the Future Workplace
|
https://www.ringcentral.com
|
[] |
Artificial intelligence tools can transform the future workplace, by reducing repetitive work, and supporting employees.
|
What is the Future of Work?
The global market for AI is projected to grow to a value of $89.8 billion by 2025. When you consider figures like that, it’s easy to see how AI could transform the future of work.
Artificial Intelligence has the power to affect everything that we do. Already, it’s changing the way that we order products, manage our home, and gather information. According to the World Economic Forum, AI machines handled about 29% of the tasks across 12 industries in 2018.
By 2022, 62% of search and data processing tasks will be managed by machines. What’s more, another study from LinkedIn found that more people are adding AI skills to their profiles, highlighting the demand for people with a passion for algorithms.
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Of course, a concept with as much power as artificial intelligence has both positives and negatives to consider. While fans of AI believe it could improve the way we work and empower everyday employees, others feel differently. There’s even a growing debate that AI could destroy the future workplace by eliminating human jobs.
So, what’s the truth about the future of work?
What is Artificial Intelligence?
Artificial Intelligence, or AI, refers to robotic algorithms and machines that can simulate human intelligence. These machines can “learn” by gathering information, use rules for reasoning, and even correct themselves when they make mistakes. In the workplace, AI exists in everything from chatbots, to intelligent speech recognition systems.
There are many different ways to design an AI system. Some stronger systems are beginning to rely less on human intervention. It’s these systems that lead to worries about robotic singularity. Some companies are even starting to introduce AI as an add-on service over the cloud. After all, AI augmentation is expected to generate $2.9 trillion in business value by 2021.
Artificial intelligence tools can transform the future workplace, by reducing repetitive work, and supporting employees. Some of the common forms of technology we see AI include:
Machine learning: A solution that encourages computers to act without programming assistance or human intervention.
A solution that encourages computers to act without programming assistance or human intervention. Automation: Robotic programming automation tools can perform repeatable high-volume tasks that give humans more time.
Robotic programming automation tools can perform repeatable high-volume tasks that give humans more time. Natural language process: The process of identifying human language to support data gathering processes and customer service.
How Are Businesses Using AI Today?
When evaluating the impact that artificial intelligence may have on the future of work, it’s essential to explore the practical uses it has in business. We can use AI in a host of different ways, from placing bots in our collaboration apps to help schedule useful meetings, to enhancing our IVR systems. Around 63% of companies think that the pressure to reduce costs will push them to invest in artificial intelligence going ahead.
Another 54% of executives say that the AI solutions they’ve already adopted have increased productivity.
Here are some of the most impactful ways that AI is changing the future of work:
1. Fighting Fraud and Cybercrime
Countless companies are struggling in the ongoing war against cybercrime. Fraud detection depends heavily on the ability to recognise patterns. Machines are capable of detecting trends in behaviour and application usage. Companies are already using machine learning techniques to develop solutions that help companies to identify suspicious activities.
Top industry security rating Comprehensive security The RingCentral platform utilises multiple layers of security to protect your data and communications while also guarding against fraud and service abuse. Find out more
Though it will take time to perfect cybersecurity solutions that allow companies to fight cybercrime, tools are becoming increasingly powerful. Some AI can even detect specific nuances in a person’s voice and use biometric markers to prevent fraud.
2. Conversational AI
The rise of conversational AI is also particularly relevant to the future of work. Companies are developing speech-based assistants like Amazon Alexa that are capable of responding to human language, as well as IVR systems and chatbots.
Through conversational AI, businesses can deliver an enhanced experience to their customers, while taking some of the stress away from human employees. Conversational AI improves the interactions you can have with your consumers through multiple touchpoints. Currently, the industry is set to reach a value of $11 billion by 2023.
3. Bots and Virtual Assistants
Increasingly, machine learning algorithms and artificial intelligence are moving beyond the basic understanding of the text. Today’s machines can also derive meaning from speech, pictures, and video. These systems are the basis of the virtual assistants that employees can use to streamline work performance.
Virtual assistants have the power to do a range of things for today’s companies. They can order supplies the moment they notice that the system is running low. The chatbots can also set up meetings and automate reminders for workers. Once in a meeting, a bot can even take notes and offer real-time transcriptions. Many leaders in the artificial intelligence space believe that virtual assistants will transform the future workplace by giving every employee the support they need.
4. AI Recruitment and Talent Sourcing
An AI-powered recruitment platform enables businesses to hire top talents worldwide with a faster turnaround time. Artificial intelligence takes over the tedious manual work needed to find, evaluate, and filter candidate profiles hence allowing companies to save a tremendous amount of time and resources. Applicants also find it easier to search for job openings that match their exact credentials, skills, and experience.
These benefits on both the candidate and the employer’s side make the hiring process a lot more efficient. AI recruitment reduces the time to hire while cutting down the hiring costs and maintaining a high quality of hire.
The Advantages of AI Tech in the Future Workplace
There’s no one-size-fits-all strategy to using artificial intelligence for the future of work.
The way you implement solutions will depend on which parts of your business need the most support. Effective AI strategies emerge when you start by defining employee pain points, then work backwards. Artificial intelligence can handle everything from data analysis to improved customer experience.
Huge organisations like Walmart are already using artificial intelligence to address things like data analytics and make more informed data-driven decisions.
How can you use artificial intelligence to positively influence the future of work?
1. Reducing Costs
80% of tech and business leaders believe that AI improves productivity. More productive employees mean better customer service and improved revenue. It can also eliminate the repetitive tasks that cost staff members time, and business leaders money.
If a task can be broken up into simple sub-tasks, then most of those tasks can be automated. For instance, machines can review security footage to look for specific indicators of suspicious activities. These tools can also automatically renew and request materials in a supply chain. When computers handle the boring and repetitive tasks in a workplace, human beings have the freedom to focus on more creative jobs and unlock their real potential.
2. Improving Efficiency
When it comes to the future workplace, voice assistants don’t replace anyone’s jobs. Instead, they’re there to add more value to the people who are already employed within a team. For instance, a virtual assistant in the office collaboration tool can automatically manage tasks, adapt schedules, set-up meetings and more. This means that employees have more time to work on the projects that matter most to them.
3. Better Customer Service
Customer experience is the most critical differentiator in any business today. Customers don’t judge a company by the price of their products or the range of services that they offer. Instead, your clients want to know that you will deliver the experience that they need on any platform. Unfortunately, it’s notoriously difficult for companies of all sizes to keep track of their customers across everything from SMS, to instant messaging, phone calls, and more.
Artificial intelligence can deliver an era of better customer experience by ensuring people get the right response at the right time. Intelligent chatbots and IVR solutions answer simple customer questions and send complex queries to the correct agent using smart routing. The result is happier customers and less stressed employees.
How to Implement Artificial Intelligence in Your Workplace
Smart technology isn’t just changing our homes anymore.
These tools are quickly making their way into numerous industries around the world, disrupting the future of work. Many fear that artificial intelligence will lead to machines taking over from humans. However, the truth is that robotics can be a powerful addition to the workplace. All you need to do is figure out how you’re going to use the technology correctly. How to prepare for the future of work:
1. Focus on AI to Complement, Not Replace
Perhaps the biggest fear associated with the future workplace and AI is that the technology will eliminate the value of humans in the office. However, while it’s true that intelligence can reduce the need for costly human labour, the aim is rarely to replace people completely.
People still demand the option to talk to human beings when they’re looking for customer service. Capgemini studies have even found that AI leads to increases in job opportunities, alongside improved service, and enhanced efficiency. Think about how AI can complement, not replace your day-to-day operations, and go from there.
2. Educate Both Your Team and Yourself
New technologies and innovations usually come with some manner of the knowledge gap. While there are definitely early adopters out there, there’s a good chance that you’re going to need basic education to get you started with artificial intelligence.
With AI set to have a significant impact on the workforce in the next five years, it’s important to think carefully about what kind of tools you need. The aim shouldn’t be to simply implement the latest tools because they’re novel and exciting. Look for strategies that will deliver measurable insights for your business.
3. Work with Specialists
When you’re looking for a vendor to support your artificial intelligence strategy, search for a company that can offer guidance. Leading businesses like RingCentral can help you with the training and insights you need to see how artificial intelligence will have a positive impact on the future of work for your company.
You’ll also need a brand like RingCentral to ensure that you can adjust your artificial intelligence strategies dynamically to suit your needs. Make sure that you’re capable of adding new solutions and services as your organisation continues to grow.
Will Humans and Machines Be Able to Work Side by Side in the Future of Work?
For many companies today, artificial intelligence represents an exciting opportunity to improve efficiency and enhance business performance. However, it’s hard to overlook the growing fear that these tools will also destroy the future of work for many employees. The concern that artificial intelligence will automate and eliminate jobs has been growing over the years.
One Oxford study claimed that around 47% of US workers may see their jobs being automated in the next 20 years. However, while it’s difficult to predict the future, the truth is that it’s unlikely that bots will ever replace human beings. Although artificial intelligence tools can definitely supplement human workers and make it easier for them to complete their tasks. There will always be a need for human creativity, innovation, compassion and intuition in the workplace.
Artificial Intelligence can do a lot of things, but bots can’t completely replace or imitate human workers. More often than not, these tools will instead be implemented to give more power back to human employees, by automating the tasks that take up too much of their time.
What’s more, artificial intelligence is also creating a slew of new jobs in many areas that weren’t around before. STEM data scientists are incredibly sought-after today, but they barely existed a decade ago.
Although some experts predict that certain repetitive jobs may be wiped out one day in the future, Gartner believes that artificial intelligence will create more jobs than it destroys. For now, the relationship between human workers and AI is likely to be a symbiotic one.
For the future workplace to be successful, it’s likely that humans and bots will need to work together to encourage positive outcomes. Instead of worrying about the singularity, it might be time to start considering the concept of multiplicity, where combinations of machines and people can work together to create innovations that we couldn’t have possibly imagined before.
Are you ready to discover what artificial intelligence can do for the future of work in your business? Reach out to the team at RingCentral today.Learn more
| 2019-05-17T00:00:00 |
2019/05/17
|
https://www.ringcentral.com/gb/en/blog/future-of-work/
|
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Robots 'to replace up to 20 million factory jobs' by 2030 - BBC
|
Robots 'to replace up to 20 million factory jobs' by 2030
|
https://www.bbc.com
|
[] |
A huge acceleration in the use of robots will affect jobs around the world, Oxford Economics says.
|
Robots 'to replace up to 20 million factory jobs' by 2030
26 June 2019 Share Save Share Save
Getty Images
Up to 20 million manufacturing jobs around the world could be replaced by robots by 2030, according to analysis firm Oxford Economics. People displaced from those jobs are likely to find that comparable roles in the services sector have also been squeezed by automation, the firm said. However, increasing automation will also boost jobs and economic growth, it added. The firm called for action to prevent a damaging increase in income inequality.
Rise of the robots
Each new industrial robot wipes out 1.6 manufacturing jobs, the firm said, with the least-skilled regions being more affected. Regions where more people have lower skills, which tend to have weaker economies and higher unemployment rates anyway, are much more vulnerable to the loss of jobs due to robots, Oxford Economics said.
Moreover, workers who move out of manufacturing, tend to get new jobs in transport, construction, maintenance, and office and administration work - which in turn are vulnerable to automation, it said. On average, each additional robot installed in those lower-skilled regions could lead to nearly twice as many job losses as those in higher-skilled regions of the same country, exacerbating economic inequality and political polarisation, which is growing already, Oxford Economics said.
We've seen plenty of predictions that robots are about to put everyone, from factory workers to journalists, out of a job, with white collar work suddenly vulnerable to automation. But this report presents a more nuanced view, stressing that the productivity benefits from automation should boost growth, meaning as many jobs are created as lost. And while it sees the robots moving out of the factories and into service industries, it's still in manufacturing that the report says they will have the most impact, particularly in China where armies of workers could be replaced by machines. Where service jobs are under threat, they are in industries such as transport or construction rather than the law or journalism and it's lower-skilled people who may have moved from manufacturing who are vulnerable. The challenge for governments is how to encourage the innovation that the robots promise while making sure they don't cause new divides in society.
Oxford Economics also found the more repetitive the job, the greater the risk of its being wiped out. Jobs which require more compassion, creativity or social intelligence are more likely to continue to be carried out by humans "for decades to come", it said. The firm called on policymakers, business leaders, workers, and teachers to think about how to develop workforce skills to adapt to growing automation. About 1.7 million manufacturing jobs have already been lost to robots since 2000, including 400,000 in Europe, 260,000 in the US, and 550,000 in China, it said.
The firm predicted that China will have the most manufacturing automation, with as many as 14 million industrial robots by 2030. In the UK, several hundreds of thousands of jobs could be replaced, it added. However, if there was a 30% rise in robot installations worldwide, that would create $5 trillion in additional global GDP, it estimated. At a global level, jobs will be created at the rate they are destroyed, it said.
| 2019-06-26T00:00:00 |
https://www.bbc.com/news/business-48760799
|
[
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"query": "robotics job displacement"
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|
Report: Robots Will Replace 20 Million Manufacturing Jobs by 2030
|
Robots Will Replace 20 Million Jobs by 2030, Oxford Report Finds
|
https://www.usnews.com
|
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"Alexa Lardieri",
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"Sign Up To Receive The Latest Updates U.S. News",
"World Report",
"Our Trusted Partners",
"Sponsors. Clicking Submit",
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The rise of robots and automation is projected to lead to the displacement of 20 million manufacturing jobs by 2030. A report from Oxford ...
|
The rise of robots and automation is projected to lead to the displacement of 20 million manufacturing jobs by 2030.
A report from Oxford Economics estimates that about 8.5% of the global manufacturing workforce stands to be replaced by robots, with about 14 million manufacturing jobs lost in China alone.
The number of robots currently in the global workforce, 2.25 million, has multiplied threefold over the past 20 years, doubling since 2010. According to the report, every third robot is installed in China. The country accounts for about 20% of robots worldwide. Additionally, since 2004 each new robot installed in the manufacturing sector has displaced an average of 1.6 workers.
Over the next decade, the U.S. is projected to lose more than 1.5 million jobs to automation. China is slated to lose almost 12.5 million, the European Union will lose nearly 2 million jobs and South Korea will lose almost 800,000. Other countries around the world are expected lose 3 million jobs to robots by 2030.
In the United States, Oregon is the most vulnerable state for job displacement, followed by Louisiana , Texas , Indiana and North Carolina . Hawaii is the safest state from robot displacement, followed by the District of Columbia, Nevada , Florida and Vermont .
The three biggest reasons for the robot surge, the report outlines, are cost, capability and the rise in demand for manufactured goods.
Internationally, rural areas in the United Kingdom are most vulnerable to automation due to their concentrated manufacturing industries. London is among the least vulnerable cities. Seoul is the least at risk in South Korea because of its diverse economy and low dependence on manufacturing jobs, while the country's Daegu region is the most vulnerable because of its low level of manufacturing productivity, leaving it "ripe for change."
The cost of machinery has drastically declined, making robots cheaper than humans. Processing power of microchips, smarter networks and longer battery life have dramatically increased the per-unit value of robots, and their costs have dropped 11% from 2011 to 2016.
Robots are also quickly becoming more capable with improvements in technology. Artificial intelligence allows robots to learn and make informed decisions and carry out more sophisticated processes. The improvements have expanded robots' usefulness in areas beyond the automotive industry.
Lastly, due to the rise in manufacturing demand, countries, especially China, are investing in robots to increase production. If the expansion of robots continues to grow at its current rate, China will have close to 8 million robots in use by 2030.
The repercussions of the robot expansion will be disproportionately felt by lower-skilled workers and poorer local economies. According to researchers from Oxford, the rise in automation will "aggravate social and economic stresses from unemployment and income inequality in times when increasing political polarization is already a worrying trend."
Politicians will face increased pressure, the report states, as robots expand and inequality worsens.
"As the pace of robotics adoption quickens, policy-makers will be faced with a dilemma: while robots enable growth, they exacerbate income inequality," researchers state in the report. "Automation will continue to drive regional polarization in many of the world's advanced economies, unevenly distributing the benefits and costs across the population. This trend will intensify as the impact of automation on jobs spreads from manufacturing to the services sector, making questions about how to deal with displaced workers increasingly critical."
| 2019-06-26T00:00:00 |
2019/06/26
|
https://www.usnews.com/news/economy/articles/2019-06-26/report-robots-will-replace-20-million-manufacturing-jobs-by-2030
|
[
{
"date": "2019/06/26",
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"query": "robotics job displacement"
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"date": "2019/06/26",
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"date": "2019/06/26",
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"date": "2019/06/26",
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How Robots Change the World - Oxford Economics
|
How Robots Change the World
|
https://www.oxfordeconomics.com
|
[] |
We estimate up to 20 million manufacturing jobs are set to be lost to robots by 2030. Read the report. The effects of these job losses will vary ...
|
Recent Release | How Robots Change the World Thought Leadership and Economic Consulting Teams Oxford Economics
What automation really means for jobs and productivity
The robotics revolution is rapidly accelerating, as fast-paced technological advances in automation, engineering, energy storage, artificial intelligence and machine learning converge. The far-reaching results will transform the capabilities of robots and their ability to take over tasks once carried out by humans.
Already, the number of robots in use worldwide multiplied three-fold over the past two decades, to 2.25 million. Trends suggest the global stock of robots will multiply even faster in the next 20 years, reaching as many as 20 million by 2030, with 14 million in China alone. The implications are immense, and the emerging challenges for policy-makers are equally daunting in scale.
The rise of the robots will boost productivity and economic growth. And it will lead to the creation of new jobs in yet-to-exist industries. But existing business models in many sectors will be seriously disrupted and millions of existing jobs will be lost. We estimate up to 20 million manufacturing jobs are set to be lost to robots by 2030.
The effects of these job losses will vary greatly across countries and regions, with a disproportionate toll on lower-skilled workers and on poorer local economies. In lower-skilled regions, we find that robots lead to almost twice as many manufacturing job losses. In many places, the impact will aggravate social and economic stress in times when political polarisation is a worrying trend.
At Oxford Economics, our mission is to help our clients better understand an ever-more complex and fast-changing world economy. With the world on the cusp of this new industrial revolution, we are pleased to share the findings of our extensive research study into these profound economic shifts with everyone interested in the shape of things to come.
That is why we brought together a team of our economists, econometricians, modellers and technology experts from across our worldwide network of over 300 economists and analysts to conduct an extensive research study to analyse the robotics phenomenon.
About the team
Our economic consulting and thought leadership teams are world leaders in quantitative economic analysis and original, evidence-based research, working with clients around the globe and across sectors to build models, forecast markets, run extensive surveys, and evaluate interventions using state-of-the art techniques.
James Lambert Director of Economic Consulting, Asia Linkedin Profile Close Get in touch Linkedin James Lambert Director of Economic Consulting, Asia Singapore James is the Director of Oxford Economics’ economic consulting services in Asia. James moved to this role from Oxford Economics’ London office, where he headed up a team dedicated to exploring the economic impact of technology. He delivered high profile studies on the growth of the digital economy, the impact of automation and the implications for the labour market. Prior to joining Oxford Economics, James spent over six years in the Government Economics Service. He worked in economics teams of the Cabinet Office, the Foreign and Commonwealth Office and the Department for Transport. There, he gained experience in microeconomic analysis and impact assessment as well as international macroeconomics, economic risk analysis and energy security. In the FCO, James spent three years working on economic issues in East and South East Asia. He also previously worked for the International Labour Organization. Edward Cone Editorial Director, Thought Leadership Linkedin Profile Close Get in touch Linkedin Edward Cone Editorial Director, Thought Leadership New York, United States Editorial Director Edward Cone oversees global research programs for our Thought Leadership group. As Technology Practice Lead he works with clients such as Google, Accenture, IBM, SAP, and many others. His areas of focus include Artificial Intelligence, the impact of technology on business performance, and healthcare organizations. Edward joined the firm in 2012 after more than two decades as a business and technology journalist based in New York, Paris, and North Carolina, including stints as an editor and writer at various Ziff Davis magazines (CIO Insight, Baseline), a contributing editor at Wired, and a staff writer at Forbes. Edward also wrote a weekly newspaper opinion column for many years in his hometown of Greensboro, NC and authored a semi-popular blog. He has contributed to a bewildering variety of magazines and papers on topics ranging from politics to rock climbing and was a frequent speaker and organizer at new media conferences across the country. Honors for his work include the 2020 Rybczynski Prize, awarded for the best essay on economics by Society of Professional Economists, and various awards from the American Society of Business Publication Editors and the North Carolina Press Association. He has a BA from Haverford College.
In 2024, five years after the report’s release, James Lambert, Director of Economic Consulting for Asia, reviews the predictions from our original whitepaper and outlines the path forward in his latest blog. Click here to read the updates.
| 2019-06-26T00:00:00 |
2019/06/26
|
https://www.oxfordeconomics.com/resource/how-robots-change-the-world/
|
[
{
"date": "2019/06/26",
"position": 66,
"query": "robotics job displacement"
}
] |
AI Job Market Cools Off to a Steady Boil - IEEE Spectrum
|
AI Job Market Cools Off to a Steady Boil
|
https://spectrum.ieee.org
|
[
"Tekla S. Perry"
] |
Employers are hiring more AI experts than last year, but the AI job market isn't growing as quickly as it once was ; 1. Machine learning engineer.
|
Huge shortages, astronomical salaries, raids on engineering departments at universities—this has been the state of the job market for AI and machine learning experts for the past few years. And with AI technology finding use in new industries almost daily, it seemed demand for AI and machine learning expertise would never slack. But has it?
Job search site Indeed looked for the answer to that question in its annual review of AI job postings. And Indeed concluded that it just might be seeing a slowdown—that is, if you call 29 percent growth a slowdown.
The number of AI jobs listed on the site from May 2018 to May 2019 increased by 29 percent over the same period a year earlier. But that’s significantly less than the increase in the previous period, which was 58 percent over 2016 to 2017. And it represents a huge drop from 2016 to 2017, when AI job postings jumped 136 percent over the previous year’s count.
While the Indeed study didn’t quantify the gap between job openings and job seekers, its data suggests that the gap is growing, and the shortage is worsening. That’s good news for engineers with AI expertise, and bad news for companies that need to hire them. The number of AI-related job searches by candidates on Indeed dropped in the May 2018 to May 2019 window, down 15 percent from the previous year. By contrast, the 2017 to 2018 period saw searches up 32 percent from the previous year.
Indeed’s review also attempted to sketch a picture of what skills these AI jobs require by looking at keywords in job listings. In the 2017 to 2018 period, the phrase “machine learning engineer” dominated the keyword rankings, and was listed in 94 percent of AI job openings. That number dropped to 75 percent in the most recent time period. Meanwhile, “deep learning engineer,” defined as engineers that develop programming systems that mimic brain functions, was listed in 62 percent of AI job postings, putting it into the top 10 for the first time. And several jobs related to data science also moved into the top 10 [see table, below]. Were these merged into one category, data scientist might have ranked higher than machine learning engineer.
While this Indeed report didn’t provide an exhaustive look at salaries, it did consider the highest paid positions. By that ranking, machine learning engineer, at an average salary of US $142,859 annually, topped data scientist, at $126,927.
Top 10 jobs involving AI skills, according to Indeed:
| 2019-06-28T00:00:00 |
2019/06/28
|
https://spectrum.ieee.org/ai-job-growth-slows-slightly-but-shortages-continue
|
[
{
"date": "2019/06/28",
"position": 73,
"query": "machine learning job market"
},
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"date": "2019/06/28",
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"query": "machine learning job market"
},
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},
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{
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"query": "machine learning job market"
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{
"date": "2019/06/28",
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"query": "machine learning job market"
}
] |
Why Workforce Reskilling is Crucial in the Age of AI and RPA - ELEKS
|
Why Workforce Reskilling is Crucial in the Age of AI and RPA
|
https://eleks.com
|
[
"Olha Zhydik",
"Content Marketing Manager",
"Sam Fleming",
"Caroline Aumeran",
"Samer Awajan"
] |
Automation transforms business, savings costs yet essentially putting at risk millions of jobs. Workforce reskilling helps companies to ...
|
Partnership is a key element of the reskilling exercise
The wholesale changes underway mean that employers cannot simply run computer literacy, CPD, and other relatively minor upskilling programs. Reskilling must be as wholesale as the change faced by workers, and few companies have the resources to undertake a skill-building operation at that scale.
Instead, companies can partner with a vendor that has the technology know-how to build the smart teams that can support today’s technology-driven working environment. With the help of a partner vendor companies will be able to retain much of their existing workforce while simultaneously ensuring that their workers grow the necessary skills to keep competing in a rapidly changing world of work.
ELEKS can help companies rapidly scale up technology-centric skills, reducing resourcing costs by up to 50%. We’ll help you quickly scale up to meet your goals. Get in touch with us.
| 2019-07-04T00:00:00 |
2019/07/04
|
https://eleks.com/blog/workforce-reskilling-rpa/
|
[
{
"date": "2019/07/04",
"position": 55,
"query": "reskilling AI automation"
},
{
"date": "2019/07/04",
"position": 52,
"query": "reskilling AI automation"
}
] |
Job Automation in Middle East - Statistics and Trends [Infographic]
|
Statistics and Trends [Infographic]
|
https://www.go-globe.com
|
[] |
A recent study suggests that by 2025, nearly 30% of global jobs will be automated, with some industries experiencing higher displacement rates than others.
|
As we step into 2025, the impact of AI on jobs has become more evident across various industries. Automation is no longer a futuristic concept—it is actively reshaping the global workforce. From robotics in logistics and supply chain to automation in manufacturing jobs, companies worldwide are increasingly adopting AI-driven technologies to enhance efficiency and reduce operational costs.
However, this rapid AI-driven job transformation has sparked concerns about job loss due to artificial intelligence, with many industries experiencing significant disruptions. Workforce automation trends indicate that millions of jobs could be replaced by AI-powered systems, particularly in sectors like retail, banking, finance, and customer service.
While some jobs are at risk, the future job market trends suggest that automation will also create new employment opportunities. The key challenge lies in reskilling workers for AI jobs and ensuring that employees can adapt to the evolving demands of the digital economy. In this blog, we’ll explore job automation statistics in 2025, industries most affected, and how countries—especially in the Middle East labor market—are preparing for this shift.
1. Jobs Lost to Automation: Key Statistics and Workforce Impact
Global Job Automation Statistics in 2025
The number of jobs lost to automation statistics continues to rise as companies prioritize AI and robotics for improved efficiency. A recent study suggests that by 2025, nearly 30% of global jobs will be automated, with some industries experiencing higher displacement rates than others.
AI replacing human jobs is most noticeable in repetitive, low-skilled tasks, including data entry, manufacturing, and customer service roles.
Countries with high levels of automation in manufacturing jobs are seeing an increase in productivity but also rising unemployment concerns.
According to AI and employment trends, developed nations like the US, Japan, and Germany are heavily investing in robotics, while developing countries are still adapting to workforce automation trends.
Despite these concerns, job creation vs. job loss in automation remains a debated topic. While millions of jobs may disappear, AI and digital transformation will generate new roles, particularly in AI maintenance, cybersecurity, and AI-driven productivity growth sectors.
Jobs at Risk Due to Automation
Which jobs are most vulnerable to AI? Data from leading research firms highlight the following sectors as most at risk:
Retail and Customer Service: AI chatbots, virtual assistants, and automated checkout systems are reducing the need for human employees in customer service jobs.
Manufacturing and Supply Chain: Robotics in logistics and supply chain has replaced thousands of warehouse and assembly line workers.
Banking and Finance: AI-driven financial systems are automating risk assessments, fraud detection, and stock market predictions, impacting jobs in investment banking and financial advisory.
Healthcare Administration: While doctors and nurses remain essential, healthcare jobs and AI automation are eliminating administrative roles through AI-driven patient record systems and medical billing automation.
Industries that rely on manual, repetitive, and data-driven tasks are the most likely to be affected. However, roles requiring creativity, emotional intelligence, and problem-solving will still require human expertise, ensuring that AI will not fully replace every job.
2. Industries Most Affected by Automation
AI and Job Automation in Retail and Customer Service
Retail is one of the industries most significantly affected by AI-driven job displacement. With the rise of e-commerce automation, traditional brick-and-mortar stores are reducing their dependence on human workers:
AI-powered chatbots in customer service jobs are handling inquiries, reducing the need for human agents.
Automation's impact on the retail industry is evident through self-checkout machines and cashier-less stores, eliminating entry-level retail jobs.
AI-driven inventory management is reducing the need for stockroom staff, affecting warehouse employees.
The Role of AI in Banking, Finance, and Healthcare
Banking and finance job automation is accelerating as AI-driven systems replace human analysts:
AI algorithms now handle financial forecasting, fraud detection, and automated trading.
Robo-advisors are replacing traditional financial planners, impacting wealth management professionals.
AI is automating compliance checks and loan approvals, reducing administrative positions in banks.
Meanwhile, the healthcare jobs and AI automation shift is transforming the industry:
AI-driven diagnostic tools are improving early disease detection, reducing reliance on human radiologists.
Automated appointment scheduling and virtual consultations are reducing the need for hospital administrative staff.
AI-powered robotic assistants are supporting surgeons, making procedures more precise and reducing human error.
Robotics and Automation in Manufacturing and Supply Chain
The manufacturing sector has been at the forefront of automation in manufacturing jobs, with AI-driven robotics performing tasks more efficiently than humans:
Robotics in logistics and supply chain is optimizing warehouse management, order fulfillment, and transportation.
AI-powered predictive maintenance is preventing machine failures, reducing downtime in factories.
Smart factories are integrating AI-driven productivity growth strategies to enhance efficiency and reduce human error.
As AI and employment trends continue to evolve, these industries must balance automation with workforce adaptation. While job losses are inevitable, the emergence of AI-driven roles offers opportunities for reskilled workers in AI system maintenance, cybersecurity, and machine learning development.
3. Job Automation Statistics in the Middle East
Automation Trends in the Middle East Workforce
The Middle East labor market and automation are undergoing a significant transformation as governments and businesses embrace AI-driven solutions. With a strong focus on Middle East digital transformation and jobs, countries like the UAE, Saudi Arabia, and Qatar are leading the way in AI adoption.
AI in Gulf Cooperation Council (GCC) job market is expanding as companies implement automation to boost productivity.
The impact of AI on jobs in sectors like oil and gas, finance, and logistics is expected to reduce reliance on human labor.
Automation trends in the Middle East workforce indicate that by 2030, nearly 45% of current jobs could be automated in some form.
Governments across the region are proactively investing in AI initiatives, launching AI-driven education programs, and encouraging workforce adaptation to digital transformation. These efforts aim to mitigate job displacement and prepare workers for emerging roles in AI and digital industries.
Job Disruption and Digital Transformation in MENA
The MENA region’s rapid technological shift is affecting job markets in several key industries:
Finance and Banking: The rise of banking and finance job automation is eliminating traditional teller roles while creating new opportunities in fintech and cybersecurity.
Retail and E-commerce: AI-driven customer service chatbots and automated payment systems are reducing demand for retail employees.
Oil and Gas: The industry is increasingly adopting AI and robotics, reducing the need for manual labor while increasing demand for AI-skilled workers.
While automation may lead to job losses, many governments are investing in reskilling workers for AI jobs, ensuring that employees transition to roles in AI programming, robotics, and cybersecurity.
4. The Economic Impact of AI-Driven Job Automation
AI and Unemployment Rates: What the Data Says
The relationship between AI and unemployment rates has become a pressing issue for economists and policymakers. While automation increases efficiency and reduces costs, it also raises concerns about jobs at risk due to automation.
According to recent job automation statistics in 2025, industries with high workforce automation trends are seeing increased unemployment rates, particularly in developing economies.
AI-driven productivity growth boosts corporate profits, but businesses must balance automation with ethical workforce management to avoid mass layoffs.
Governments worldwide are considering policies such as universal basic income (UBI) and AI taxation to counteract AI-driven job displacement.
To mitigate the risks, businesses and policymakers are working on strategies that encourage workforce adaptation to digital transformation while ensuring economic stability.
The Balance Between Job Loss and Job Creation
While automation does replace certain jobs, it also creates new employment opportunities in emerging tech industries. Job creation vs. job loss in automation remains a complex debate, but data suggests that AI is generating demand for specialized skills.
The rise of AI-driven job transformation has created new roles in AI system maintenance, cloud computing, and data analytics.
Future job market trends predict that AI-focused industries will require skilled professionals in cybersecurity, machine learning, and AI ethics.
Governments and businesses are investing in reskilling workers for AI jobs, helping employees transition into high-demand careers.
Despite concerns over jobs lost to automation statistics, proactive measures such as digital education programs and AI-focused job training will help workers adapt. The key to future job market resilience lies in embracing AI while ensuring that human talent remains a crucial part of the workforce.
5. Preparing for the Future: Reskilling and Workforce Adaptation
Reskilling Workers for AI Jobs
With the rise of AI-driven job transformation, the demand for new skill sets is growing rapidly. While some industries face AI-driven job displacement, others are seeing the emergence of roles requiring expertise in AI system maintenance, cybersecurity, and data analytics. The key to mitigating the impact of jobs lost to automation statistics lies in reskilling workers for AI jobs and preparing them for the digital economy.
How Reskilling Can Help Counter Workforce Automation Trends
Upskilling programs: Companies and governments worldwide are launching training programs to equip workers with AI and digital skills.
AI and employment trends indicate that professions in machine learning, cloud computing, and robotics are in high demand.
Middle East digital transformation and jobs initiatives are helping professionals transition into AI-driven roles, particularly in countries like the UAE and Saudi Arabia.
Major organizations are investing in AI education, offering online courses and boot camps to ensure that workers remain competitive in an evolving job market. By focusing on workforce adaptation to digital transformation, businesses can bridge the skills gap and ensure sustainable employment in the AI era.
The Future of Work: AI and Human Collaboration
While automation is reshaping industries, human workers still play a crucial role in AI-powered workplaces. The future of work in an AI-driven world is not about full job replacement but about AI and human collaboration.
Key Areas Where Humans and AI Will Work Together
AI-augmented decision-making: AI can analyze vast amounts of data, but human oversight ensures ethical and strategic decision-making.
Creative industries: AI can assist in content creation and marketing, but human creativity remains irreplaceable.
Healthcare and AI: While AI improves diagnostics and patient care, doctors and nurses remain essential for human interaction and treatment.
Businesses adopting AI must strike a balance by integrating AI while ensuring that workforce automation trends support human employees rather than replace them entirely.
6. Conclusion and Future Predictions
The impact of AI on jobs is undeniable, with job automation statistics in 2025 highlighting both opportunities and challenges. While jobs at risk due to automation continue to grow, industries are also seeing the rise of new, AI-driven professions.
Key Takeaways:
Job loss due to artificial intelligence is significant in manufacturing, retail, finance, and customer service.
The Middle East labor market and automation trends indicate that GCC countries are actively reskilling their workforce to embrace AI.
Future job market trends show that AI will create new employment opportunities in cybersecurity, cloud computing, and machine learning. Governments and businesses must focus on workforce adaptation to digital transformation to ensure economic stability.
As we move further into the AI-driven era, the focus should be on job creation vs. job loss in automation and ensuring that employees are equipped with the right skills for the future. AI is not the enemy—it is a tool that, when used wisely, can enhance productivity, drive economic growth, and create sustainable career paths
| 2019-07-11T00:00:00 |
2019/07/11
|
https://www.go-globe.com/job-automation-in-middle-east-statistics-and-trends-infographic/
|
[
{
"date": "2019/07/11",
"position": 88,
"query": "job automation statistics"
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"query": "job automation statistics"
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Reskilling Human Resources For the Age of Automation - ERE.net
|
Reskilling Human Resources For the Age of Automation
|
https://www.ere.net
|
[] |
... automation and artificial intelligence (AI). In response, human resources and talent development functions are driving training initiatives ...
|
Jul 30, 2019
Pervasive digital transformation has game-changing implications for employees and the skills they need to stay relevant. According to PwC, 38% of global CEOs are extremely concerned their business growth is threatened by their workers’ lack of key skills. The World Economic Forum predicts that more than half of all workers will need reskilling and training to address the changes created by intelligent automation and artificial intelligence (AI).
In response, human resources and talent development functions are driving training initiatives, equipping employees with the skills they need to adapt and drive the business forward.
Human resources organizations are also being up-ended by digitization. We’re already seeing the impact in entry level recruiting roles replaced by smart technologies that can screen and scan candidates using algorithms, for example. Nearly all HR services can be automated to a degree. From predictive analytics that assess where future talent needs may exist to chatbots that provide benefits services to employees, the opportunity to supplement HR roles with technology is nearly endless.Who, though, is responsible for ensuring that HR teams themselves aren’t left behind, with jobs lost to – or made irrelevant by – automation and digitization?
That’s us. The human resources, talent development and people team leaders. We are staring down the task of building HR teams of the future – organizations that are agile, flexible, and can coexist effectively with tools like chatbots. Adding to this complexity is the need to meet the changing demands of today’s and tomorrow’s workforce, digitally native Gen Y [millennials] and Gen Z.
So, how do we adapt?
At Medidata, we’re on our journey to ensure our people team (our HR team) of 65 is equipped to best function in this new environment, increasing our organization’s agility for future success.
Create a people strategy
At Cisco, and now at Medidata, I’ve architected comprehensive people, or talent, strategies aligned to driving business outcomes. All our work ladders into this, keeping us focused on how we can support our people, encourage growth, collaboration and inspire them to do great work in this changing, ever-digitizing world. This strategy applies to every single employee, whether they’re in a lab or in a remote office.
At Medidata, our strategy is to accelerate our business growth and achieve our patient mission by creating exceptional talent programs and a workplace where people are inspired, risk-taking is encouraged, and innovation thrives. This comes to life in five pillars:
Build & Inspire Great Teams Create an Amazing Talent Brand and Employee Experience Thought Leadership & Innovation (for our people team) Drive Digitization and Insights Create a Better World.
In particular, our Digitization and Insights pillar addresses not only how our people connect to technology but how we utilize predictive analytics and other smart technologies to supplement and advance our work.
Train on change management
Plenty of resources exist to help build the curriculum your team needs to adapt and evolve. At Medidata, we’re exploring a range of development programs including The Bersin Academy, Future Workplace AI and My HR Academy. We’re working with industry thought leaders (in many cases, my peers at other companies) to create Journey Maps, highlighting “Moments that Matter” for our people team. And, we’re sending our teams to conferences and events like UNLEASH, HR Tech and others to learn from the industry and engage in constructive dialogues about how digitization and AI are disrupting – and creating opportunity for – HR.
Acknowledge generational differences
For the first time ever, we have five generations in the workforce. Digitally native millennials and Gen Z are becoming a larger percent of the workforce. Those groups have specific ways they like to consume information, for example, in formats like memes, quizzes and videos. Gen Y and Gen Z also expect on-demand access and support, much like we see in the consumer economy. Consider these expectations and needs in how you build connections between HR teams and employees and realize that many of your HR teams may lack the demographics of the employees they support.
Educate on AI
There’s a gap between HR knowledge of AI and our expectations of its impact. According to the TLNT.com report, What Artificial Intelligence Means for Human Resources, only 14% of HR professionals believe they are knowledgeable in AI. Yet, 46 % feel they will be using AI at a high degree by 2023. Without understanding what AI is, we are far less likely to adapt effectively. Educating your team can start with something simple – share a great article on AI in the workplace or bring a thought leader in to speak. We had the whole team read Jason Averbook’s book, The Ultimate Guide to a Digital Workforce Experience: Leap for a Purpose.
Understand the implications
Digital transformation is ongoing and thus brings uncertainty. The four steps above are helping us to manage that uncertainty and ensure our HR organization has the agile mindset we need for today, and tomorrow.
What is clear today is that as smart technologies move deeper into the core of our business, we need to ensure there is a focus on algorithms that are fair, interpretable and based on valid data sets. AI must support our goal to create belonging in the workplace.
And across the board, AI will supplement, not entirely replace humans in HR. People are the core of our business and it is our HR teams that need to be most effective in stepping into the strategy, consulting and business areas that drive growth and positive experiences for our companies. As CHROs and talent leaders, we need to stop watching the trend and start preparing our teams for their new role.
| 2019-07-30T00:00:00 |
https://www.ere.net/articles/reskilling-human-resources-for-the-age-of-automation
|
[
{
"date": "2019/07/30",
"position": 80,
"query": "reskilling AI automation"
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|
How Unions Can Protect the Workers Vulnerable to Automation
|
How Unions Can Protect the Workers Vulnerable to Automation
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https://uniontrack.com
|
[
"Ken Green",
"Uniontrack Team"
] |
“Even in the best of times, many, if not most, workers will strain to manage the coming necessary adjustments as automation and AI change or ...
|
Automation has irrevocably changed the workplace and workers’ roles in it.
There’s no arguing that the advances in workplace automation have made — and will continue to make — many jobs obsolete. According to McKinsey researchers James Manyika, et al., by the year 2030 some 800 million workers across the globe will have lost their jobs due to automation.
“Even in the best of times, many, if not most, workers will strain to manage the coming necessary adjustments as automation and AI change or eliminate many jobs, while simultaneously creating new ones,” write Robert Maxim and Mark Muro at The Brookings Institution’s Metropolitan Policy Program.
It’s a future for which many workers are unsure how to prepare. That’s where unions have to step in and lead the charge by giving workers a roadmap for navigating such a future while holding employers accountable.
Here’s are some ways organized labor can mitigate automation’s threats to workers.
Help Ensure Access to Retraining Programs to Meet Technology Demands
Adaptation is going to be the key to survival for workers in a more-automated economy. “Unions need to figure out how to help workplaces and workers adapt to new technologies to reduce layoffs if workers are to have hope of surviving and even thriving in the face of this threat,” writes labor reporter Steven Greenhouse.
Most workers, either through job change or role transition, will eventually have to learn how to work side-by-side with automated machines, says Michael Chui, partner at the McKinsey Global Institute. “People increasingly, over time, will have to be complements to the work that machines do.”
For workers, this will mean reskilling for new roles and responsibilities to stay relevant in the workforce. “The people are going to require a new set of skills to maintain their ability to support themselves and have a decent quality of life,” Susan Schurman, a labor studies professor at Rutgers University, tells Bloomberg Law.
Unions are in a position to lead the way in helping workers adapt to new technologies and reskill to prepare them for successful transitions in the automated workplace, but they must work together with employers to be effective. Daniel Bustillo, director of the Healthcare Career Advancement Program, a national network of Service Employees International Unions and healthcare employers, says unions and employers both have equal responsibility to provide retraining programs to workers.
For unions, the collective bargaining agreement is the best mechanism available to push companies for employee retraining.
Include Automation in Collective Bargaining Agreements
Unions can help secure that training for workers by including language for retraining programs in collective bargaining contracts. This holds employers accountable to their workers and helps ensure that employees continue to have a place in the workforce. Unions can also collaborate with employers to facilitate the retraining programs.
That’s the approach the Culinary Workers Union Local 226 in Las Vegas took when negotiations with hotels began in 2018. The union gained a big win by securing language to protect workers when employers bring in new technologies. Some of those protections include:
Up to six months’ notice of the adoption of new technology that could lead to layoffs and/or reduction of hours.
Free reskilling to use new technology in current jobs.
Access and free job training if any new jobs are created because of automation and technology.
The Transportation Trades Department (TTD) of the AFL-CIO is taking a similar approach by outlining eight principles for collective bargaining and legislation that transportation unions should pursue to protect workers as driverless technology matures. These principles include advanced notice before automated vehicles are deployed, a workforce training program and having drivers remain onboard driverless vehicles as a safety measure.
“We cannot allow safety to be compromised or the good jobs in this sector to be steamrolled just so tech companies and Wall Street investors can have their way,” says TTD President Larry Willis.
Fight to Protect Workers’ Wages
Automation has been shown to exacerbate income inequality and wage polarization.
Researchers Sungki Hong and Hannah Shell at The Federal Reserve Bank of St. Louis conclude that the gap widens because automation more often displaces the lowest-paid workers.
This is where the issue of automation dovetails with grassroots campaigns such as the Fight for $15. Workers must collectively demand their fair share of the profits created by automation’s efficiencies. This is also where unions must step in to protect wages and mitigate income inequality.
As a collective force, workers are better able to bargain for living wages when faced with employers looking to cut costs through automation.
Hold Employers Accountable to Fair Labor Practices
One questionable use of automation in the workforce is Amazon’s “auto-firing” of fulfillment center employees. The Verge’s Colin Lecher details how those workers’ productivity is tracked by robots that then issue warnings and termination orders based on a workers’ production.
One of the stats the system tracks is someone’s “time off task.” If a person’s time between package scans is too long, the system automatically issues warnings that can eventually lead to the person’s firing.
Termination paperwork can get generated without a human supervisor intervening at any point (though the company says the final decision to terminate is up to a manager). To avoid termination, employees report going without bathroom breaks to meet their quotas, which can be up to 100 packages an hour.
In one year, Lecher reports, Amazon used the automated system to fire approximately 300 full-time workers at a fulfillment center in Baltimore for failing to meet productivity quotas, more than 10 percent of the fulfillment center’s workforce.
“It is surreal to think that any company could fire their own workers without any human involvement,” says Marc Perrone, president of the United Food and Commercial Workers International Union. That’s the type of automation that unions can step in to mitigate through collective bargaining contracts.
These are just a few of the many threats that automation poses to jobs and workers. Unions are in a position to help protect jobs and support workers as they navigate these challenges. By using a communication tool like UnionTrack’s ENGAGE, union leaders can maintain constant contact with members to better understand those challenges and strategize approaches for dealing with the threats.
| 2019-09-17T00:00:00 |
2019/09/17
|
https://uniontrack.com/blog/unions-and-automation
|
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Are Workers Losing to Robots? - San Francisco Fed
|
Are Workers Losing to Robots?
|
https://www.frbsf.org
|
[] |
While it has increased labor productivity, the threat of automation has also weakened workers' bargaining power in wage negotiations and led to ...
|
FRBSF Economic Letter 2019-25 | September 30, 2019
The portion of national income that goes to workers, known as the labor share, has fallen substantially over the past 20 years. Even with strong employment growth in recent years, the labor share has remained at historically low levels. Automation has been an important driving factor. While it has increased labor productivity, the threat of automation has also weakened workers’ bargaining power in wage negotiations and led to stagnant wage growth. Analysis suggests that automation contributed substantially to the decline in the labor share.
A strong labor market and low unemployment traditionally help boost wages. But in the past two decades, the labor share—the portion of national income going to workers—has declined from about 63% in 2000 to 56% in 2018. This decline accelerated during the Great Recession, and the labor share has remained at historically low levels, even with strong employment growth in recent years.
One possible cause of the decline in the labor share is that workers have lost bargaining power over the years. The late economist Alan Krueger highlighted several contributing factors, such as declines in union membership, increased outsourcing and offshoring, and noncompete clauses that hinder workers’ mobility across employers and regions (Krueger 2018).
Another factor to consider is automation. Businesses have more options to automate hard-to-fill positions now than in the past. With rapid advances in robotics and artificial intelligence, robots can perform more jobs and tasks that required human skills only a few years ago. The steady decline in the relative prices of robots and automation equipment over the past few decades have made it increasingly profitable to automate. In this environment, workers may be reluctant to ask for significant pay raises out of fear that an employer will replace their jobs with robots.
In this Economic Letter, we examine the impact of automation on the labor share by looking at its effects on workers’ bargaining power. We show that the threat of automating a job weakens workers’ bargaining positions and thus restrains wage growth in a tight labor market. Although automation boosts labor productivity, the productivity gains do not fully translate into wage gains. We find that automation has contributed to a signification portion of the decline in the labor share over the past two decades. Our theory also helps explain the puzzle of stagnant wage growth in recent years.
The decline in the labor share
The labor share represents the portion of national income that goes to workers. It is the ratio of labor compensation in the form of wages and other benefits relative to the compensation of all factors of production in the economy, which is national income. For a given size of national income, a drop in labor compensation reduces the labor share.
A useful way to think about the labor share is that it is the ratio of real wages to labor productivity. As a result, the labor share would be constant if an increase in labor productivity were matched by an equal increase in real wages. However, the labor share would decline if real wages weren’t able to keep up with increases in labor productivity.
There are practical challenges in measuring the labor share. For example, it is not clear what proportion of self-employment compensation should be counted as labor income (Elsby, Hobijn, and Sahin 2013). As a benchmark, we use the measure of the labor share of the nonfarm business sector constructed by the Bureau of Labor Statistics, shown in Figure 1. The labor share fluctuates over the business cycle, but it stayed around 63% between 1985 and 2000.
Figure 1
Labor share in U.S. nonfarm business sector Source: Bureau of Labor Statistics. Gray bars indicate NBER recession dates.
Since the early 2000s, however, the labor share has fallen about 7 percentage points. About half of the drop occurred during the Great Recession. Even during the lengthy recovery and expansion, the labor share has stayed around 56%, near the historical low in our sample. The significant decline in the labor share reflects that increases in real wages have not kept up with labor productivity improvements over the past two decades.
Automation, workers’ bargaining power, and the labor share
Economists have long understood that technological improvements that make it easier to automate jobs—so that businesses can substitute capital for labor—can reduce the labor share. For instance, the British economist John Hicks noted the potential link back in the 1930s.
However, in traditional macroeconomic models, productivity improvements triggered, for instance, by automation go hand in hand with rising wages because labor markets in those models are perfectly competitive and frictionless. In other words, wages would instantly adjust until the supply of labor meets demand, leading to full employment. Workers also would be paid for how much an additional hour of work adds to production, known as their marginal products. Thus, traditional macro models predict that a technological improvement that raises workers’ productivity also raises wages. This prediction is inconsistent with recent data, though: the decline in the labor share since the early 2000s has been accompanied by stagnant wage growth. Viewed through the lens of the traditional model, this observation would cast doubts on the importance of automation (Elsby et al. 2013).
In our recent work (Leduc and Liu 2019), we revisit the link between automation and the labor share in a more realistic model of the labor market. Our model features wage bargaining in a labor market with job search frictions. These search frictions capture the reality that businesses and workers are constantly searching to find suitable employment matches and that searching is costly. Businesses need to post vacancies and interview candidates, while job seekers must comb through ads, send résumés, and interview with potential employers. This costly search process implies that there is a range of possible wage rates that businesses and workers could agree upon in forming a job match. The final wage decisions depend on the relative bargaining power between the employers and the job seekers. Also, the wage rates in general do not coincide with the workers’ marginal products. Employed workers are willing to stay in their current positions even when wages fall short of their marginal products, because they would like to avoid the costly search process necessary to find a new job.
In contrast to traditional models with perfectly competitive labor markets, our model predicts that automation can lead to a decline in the labor share, along with stagnant wage growth. Automation gives employers another option in wage negotiations and thus weakens workers’ bargaining power.
To assess the importance of automation for explaining the declines in the labor share, we estimate our model using quarterly data for unemployment, job vacancies, inflation-adjusted wage growth, and labor productivity growth. Our sample covers the period from 1985 to 2018. Fitting our model to the time series of labor productivity, along with the other labor market variables, helps quantify the role of automation. Labor productivity growth has slowed substantially since the mid-2000s and has been particularly weak since the Great Recession (Fernald 2015). However, automation has become increasingly important in recent years and should ultimately affect productivity.
We use our estimated model to evaluate the contribution of automation to the change in the labor share from 1985 to 2018. The contribution of automation is captured by the difference between the actual labor share in the data and that implied by a special scenario using our model in which the degree of automation is kept constant at its long-run average.
Figure 2 shows that the labor share in the special scenario with no changes in automation (green line) does not fluctuate over the business cycle; more importantly, the decline in the labor share would have been much more muted than in the actual data (blue line). Our model predicts that, without automation, the labor share at the end of 2018 would have stayed around 59.5%, much higher than the actual labor share of about 56%.
Figure 2
U.S. labor share: Actual versus scenario without automation Source: Bureau of Labor Statistics and authors’ calculations. Gray bars indicate
NBER recession dates.
Our model implies that the probability that businesses will automate a job position is procyclical, rising in expansions and falling in recessions, because the net benefits of automation are procyclical. If the automation probability increased in good economic times, then employers would have an alternative option to fill job openings, giving them an upper hand in wage negotiations. The resulting decline in workers’ bargaining power would act as a drag on wage increases, even if productivity improved through automation. In other words, workers would not get all the benefits of rising labor productivity. Our model implies that, if automation had not been a part of the picture over the past two decades, productivity would have risen even less than it actually did, while wages would have risen more.
Although automation weighs on the labor share in our model, it nevertheless has a positive impact on aggregate employment and thus has contributed to the steady decline in the unemployment rate in recent years. The option to automate jobs boosts the incentive for firms to create jobs, because they can adopt a robot to perform the job if the search process fails to yield a match with a worker. Therefore, our model does not predict that automation triggers a form of technological unemployment, as Keynes suggested in the 1930s. Instead, while automation eliminates certain types of jobs, it also generates new ones (see Acemoglu and Restrepo 2018).
Additional evidence
Our model predicts that increases in automation restrain wage increases and thus reduce the labor share. This prediction is in line with other independent empirical studies. For example, David Autor and Anna Salomons (2018) used data from 28 industries across 18 developed countries to show that automation has had a significant negative impact on the labor share, particularly since the early 2000s. They also find that automation did not reduce employment in their sample, consistent with our findings.
The predictions from our model are also consistent with evidence at the establishment level. For instance, Dinlersoz and Wolf (2018) use data from the 1991 U.S. Census Bureau’s Survey of Manufacturing Technology to document that business establishments with more investment in automation experienced greater productivity gains but also larger declines in their labor shares.
Conclusion
The labor share in the United States has declined roughly 7 percentage points over the past two decades. The decline started in the early 2000s and accelerated during the Great Recession. After the recession, the labor share failed to bounce back despite strong employment gains, particularly over the past few years.
In this Letter we argue that automation may have been partly to blame. Having the option to automate jobs strengthens firms’ bargaining power against workers. This keeps wage increases stagnant despite productivity gains. We find that automation contributed substantially to the decline in the labor share since the early 2000s.
Sylvain Leduc is executive vice president and director of research in the Economic Research Department of the Federal Reserve Bank of San Francisco.
Zheng Liu is senior research advisor and director of the Center for Pacific Basin Studies in the Economic Research Department of the Federal Reserve Bank of San Francisco.
References
Acemoglu, Daron, and Pascual Restrepo. 2018. “The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment.” American Economic Review 108, pp. 1,488–1,542.
Autor, David, and Anna Salomons. 2018. “Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share.” Brookings Papers on Economic Activity, Spring, pp. 1–87.
Dinlersoz, Emin, and Zoltan Wolf. 2018. “Automation, Labor Share, and Productivity: Plant-Level Evidence from U.S. Manufacturing.” U.S. Census Bureau Center for Economic Studies Working Paper, pp. 18-39.
Elsby, Michael, Bart Hobijn, and Aysegul Sahin. 2013. “The Decline of the U.S. Labor Share.” Brookings Papers on Economic Activity, Fall, pp. 1–63.
Fernald, John G. 2015. “Productivity and Potential Output before, during, and after the Great Recession.” Chapter 1 in NBER Macroeconomics Annual 2014, volume 29, eds. Jonathan Parker and Michael Woodford. Chicago: University of Chicago Press, pp. 1–51.
Krueger, Alan. 2018. “Reflections on Dwindling Worker Bargaining Power and Monetary Policy.” Luncheon address to FRB Kansas City’s Jackson Hole Symposium, August 24.
Leduc, Sylvain, and Zheng Liu. 2019. “Robots or Workers? A Macro Analysis of Automation and Labor Markets.” FRB San Francisco Working Paper 2019-17.
| 2019-09-30T00:00:00 |
2019/09/30
|
https://www.frbsf.org/research-and-insights/publications/economic-letter/2019/09/are-workers-losing-to-robots
|
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The 10 media companies that will lead Artificial Intelligence ...
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The 10 media companies that will lead Artificial Intelligence Journalism in the next decade
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https://www.linkedin.com
|
[] |
Artificial Intelligence (AI) Journalism as a new technological concept based on the techniques of the Fourth Industrial Revolution benefits ...
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Artificial Intelligence (AI) Journalism as a new technological concept based on the techniques of the Fourth Industrial Revolution benefits all fields, not only the media. AI Journalism needs to be accepted and integrated into the media companies, according to the companies’ acceptance of the technologies, in accordance with their human capacities that facilitate and accelerate the use of such instruments.
Over the next ten years, we will find media organizations relying on Artificial Intelligence Journalism to analyze Big Data, use data extensively in their reports, store all information and “archive” what we can call “The World Future Data.”
In my new book “Artificial Intelligence Journalism, 4IR and Media Restructuring “ I have classified some world media companies that have begun to take serious steps to utilize the tools of Artificial Intelligence Journalism, which will lead to the implementation of this new media revolution over the next decade:
AP: According to the AP experience, media starts to search for the best ways to transmit Big data of news on a daily basis and to obtain numbers and statistics supporting the news or content. AP uses data scientist in newsrooms. Depending more on Automated Insights’ platform, AP were able to automate their quarterly earnings reports – with astronomically improved results. It increased output tenfold, generating more than 3,000 stories per quarter compared to just 300 previously. The Washington Post: The Washington Post also has Artificial intelligence Journalism tool “robot reporting program” called Heliograf. In its first year, it produced approximately 850 articles and earned The Post an award for its “Excellence in Use of Bots” from its work on the 2016 election coverage. However, The Post is using their system to not replace journalists, but to assist them and make their jobs easier and faster.
Bloomberg: Bloomberg News uses some form of Artificial intelligence Journalism tools, and automated technology. Created by Cyborg Company that tool has the ability to assist reporters in churning out thousands of articles each quarter. Around a third of the content published by Bloomberg News is producing by these tools.
The New York Times: The American newspaper is using one of the Artificial intelligence Journalism tool to personalize newsletters, to help with comment moderation and identify images as it digitizes its archive, to serve readers a newsletter containing stories they “might have missed.”
BBC: The BBC has created its Artificial intelligence Journalism tool is called “Juicer”, which is a news aggregation and content extraction API. It takes articles from the BBC and other news sites, automatically parses them and tags them with related DBpedia entities. The entities are grouped in four categories: people, places, organisations and things .
Thomson Reuters: Thomson Reuters leads a huge initiative in Artificial intelligence Journalism, when it announced the launch of Reuters - Lynx Insight, a major new Artificial intelligence Journalism tool that will be used in its newsrooms across the world. Lynx Insight aims to help journalists in analyzing data, suggesting story ideas, and even write some sentences, aiming not to replace reporters but instead augment them with a digital data scientist-cum-copywriting assistant.
Xinhua News Agency: State news outlet Xinhua recently announced that it had, in collaboration with search engine Sogou, created the world’s first female AI news anchor, known as Xin Xiaomeng. The anchor will make “her” debut during the upcoming Two Sessions political meetings at the start of March. The announcement comes after Xinhua debuted the world’s first male AI news anchor, QiuHao, during China’s annual World Internet Conference held in November 2018.
The Wall Street Journal: Deepfakes, one of the Artificial intelligence Journalism tool in The Wall Street Journal, was launched by deepfakes task force led by the Ethics & Standards and the Research & Development teams. This group, the WSJ Media Forensics Committee, is comprised of video, photo, visuals, research, platform, and news editors who have been trained in deep fake detection. Facebook: Worldwide, there are over 2.38 billion monthly active users (MAU) in Facebook as of March 31, 2019. In addition, Twitter Monthly Active Users reached 330 million in Q1 2019. It is important that these platforms use Artificial Intelligence Journalism tools, to contribute in publishing the right content, and to fight fake news. Facebook has begun to use Machine learning tool, or we can say one of AI Journalism tools to check t fake news and data.
Twitter: Twitter also states that AI is a key tool in the battle against fake news, saying that the company is “investing heavily” in technology to tackle fake accounts and manipulation.
Artificial Intelligence race is still going on, and the Big Media and social media companies everyday are updating their tools and mechanism to achieve the best practices in AI Journalism.
#AI_Journalism
#Robotisation_of_Marketing
#Fake_News_industry
#Mohamed_Abdulzaher
#صحافة_الذكاء_الاصطناعي
#محمد_عبد_الظاهر
#Artificial_Intelligence_Journalism
#UAE
#BBC
#Reuters
#Facebook
#New_York_Times
#Xinhua_News
#Wall_Street_Journal
#Media
#Twitter
#AP
#Bloomberg
| 2019-10-01T00:00:00 |
https://www.linkedin.com/pulse/10-media-companies-lead-artificial-intelligence-next-abdulzaher
|
[
{
"date": "2019/10/01",
"position": 84,
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|
The economics of Artificial Intelligence today | Nesta
|
The economics of Artificial Intelligence today
|
https://www.nesta.org.uk
|
[
"Juan Mateos-Garcia",
"Director Of Data Analytics Practice",
"Juan Mateos-Garcia Was The Director Of Data Analytics At Nesta.",
"View Profile"
] |
Third, there is capital deepening. New AI systems are an investment that increases the stock of capital that workers use, making them more ...
|
Economists have been studying the relationship between technological change, productivity and employment since the beginning of the discipline with Adam Smith’s pin factory. It should therefore not come as a surprise that AI systems able to behave appropriately in a growing number of situations - from driving cars to detecting tumours in medical scans - have caught their attention.
In September 2017, a group of distinguished economists gathered in Toronto to set out a research agenda for the Economics of Artificial Intelligence (AI). They covered questions such as what is economically unique about AI, what will be its impacts, and what are the right policies to enhance its benefits. I recently had the privilege of attending the third edition of this conference in Toronto, and to witness first-hand how this agenda has evolved in the last two years. In this blog I outline key themes of the conference and relevant papers at four levels: macro, meso (industrial structure), micro and meta (impacts of AI on the data and methods that economics use to study AI). I then outline some gaps in today's Economics of AI agenda that I believe should be addressed in the future, and conclude.
Prelude: an economist’s take on AI
Ajay Agrawal, Joshua Gans and Avi Goldfarb, the convenors of the conference (together with Catherine Tucker), have in previous work described AI systems as ‘prediction machines’ that make predictions cheap and abundant, enabling organisations to make more and better decisions, and even automating some of them. One example of this is Amazon’s recommendation engine, which presents a personalised version of its website to each visitor. That kind of customisation would not be possible without a machine learning system (a type of AI) that predicts automatically what products might be of interest to individual customer based on historical data. AI systems can in principle be adopted by any sector facing a prediction problem - which is almost anywhere in the economy from agriculture to finance. This widespread relevance has led some economists to herald AI as the latest example of a transformational ‘General Purpose Technology’ that will reshape the economy like the steam engine or the semiconductor did earlier in history.
Macro view: AI, Labour and (intangible) capital
AI automates and augments decisions in the economy, and this increases productivity. What are the implications for labour and investment? Who - or what - does what: The task-based model The dominant framework to analyse the impact of AI on labour is the task-based model developed by Daron Acemoglu and Pascual Restrepo (building on previous work by Joseph Zeira). This model conceives the economy as a big collection of productive tasks. The arrival of AI changes the value and importance of these tasks, impacting on labour demand and other important macroeconomic variables such as the share of income that goes to labour, and inequality (for example, if AI de-skills labour or increases the share of income going to capital - which tends to be concentrated in fewer hands - this is likely to increase income and wealth inequality). The impact of AI on tasks takes place through four channels: First, there is displacement, when an AI system replaces some of the tasks that were previously performed by human workers. An example of this would be the book reviews that were displaced when Amazon adopted its automatic recommender (and laid off its book reviewers, although some have now made it back to the company). This will reduce the demand for labour Second, there is augmentation when an AI system increases the value of the tasks undertaken by human workers. An example of this would be Amazon’s web development and inventory management tasks: each dollar spent on improving its website and ensuring that many different titles are efficiently stocked creates a bigger return for the company thanks to its AI recommendation system. This will in general increase the demand for workers whose tasks are augmented. Third, there is capital deepening. New AI systems are an investment that increases the stock of capital that workers use, making them more productive and increasing demand for labour through the same mechanism as above. Finally, there is reinstatement, when the AI system creates completely new tasks such as developing machine learning systems or labelling datasets to train those systems. These new tasks will create new jobs and even industries, increasing labour demand. Considered together, these four channels determine the impact of AI on labour demand. Contrary to the idea of the impending job apocalypse, this model identifies several channels through which AI systems that increase labour productivity could also increase demand for labour. Contrary to previous assumptions by economists that new technology always increases labour demand through augmentation, the task-based model recognises that the net effect of new technology on labour demand could be negative. This could, for example, happen if firms adopt ‘mediocre’ AI systems that are productive enough to displace workers, but not productive enough to increase labour demand through other channels. Several papers presented in the conference built on these themes: Jackson and Kanik model AI as an intermediate input that firms acquire through their supply chain using services such as Amazon’s Web Services. In this model, the impact of AI on labour demand and productivity depends on the outside options of those workers who are displaced by AI: if alternative jobs have low productivity, then this will have an (indirectly) negative impact on productivity. This means that the impacts of AI depend not only on what happens in AI adopting sectors but also in the situation elsewhere in the economy. Another interesting conclusion of their analysis is that AI deployment makes the economy more interconnected as companies start using AI suppliers to source services previously performed by workers. This could centralise value chains, increasing market power and creating systemic risks.
Autor and Salomons study the evolution of the industries and occupations that create new job titles (a proxy for new tasks) using a dictionary of job titles published by the US Census since the 1950s. Their analysis shows important changes between that time, when occupations towards the middle of the income distribution (‘middle class jobs’) created most new job titles, and today, when most of the new job titles are created in either highly skilled, technology intensive occupations (eg software development) or less skilled personal services occupations (eg personal trainers). It seems that modern technologies like AI are enabling the creation of new tasks that increase demand for high skilled jobs that complement AI and low-skill jobs that are difficult to replace with AI, leading to polarisation in the labour market. This result underscores the risk that skills shortages in highly-skilled occupations (which hinder productivity growth) might coexist with unemployment amongst individuals lacking the skills to transition into those occupations.
Automation without capital: Intangible investments In order to increase productivity, investments in AI needs to be accompanied by complementary investments in IT infrastructure, skills and business processes. Some of these investments involve the accumulation of ‘intangibles’ such as data, information and knowledge. In contrast to tangible assets like machines or buildings, intangibles are hard to protect, imitate and value, and their creation often involves lengthy and uncertain processes of experimentation and learning-by-doing (much more on this subject here). Continuing with the example of Amazon, over its history the company has built a tangible data and IT infrastructure complementing its AI systems. At the same time, it has developed processes, practices and a mindset of ‘customer-centrism’ and ‘open interfaces’ between its information systems and those of its vendors and users of its cloud computing services which could be equally important for its success, but very hard to imitate. According to a 2018 paper by Erik Brynjolfsson and colleagues, the need to accumulate these intangibles across the economy explains why advances in AI are taking so long to materialise in productivity growth or drastic changes in labour demand. Several papers presented in Toronto this year explored these questions empirically: Daniel Rock uses LinkedIn skills data to measure the impact of engineering skills on firm value. He finds that after controlling for unobservable firm factors, the impact of those skills on value dissipate, suggesting that intangible firm factors determine how much value a firm is able to create from its engineering talent. His analysis also shows that the market expects these intangible investments to create value in the future: when Google released TensorFlow, an AI software programme, those firms already employing AI talent saw their market value increase. This is consistent with the idea that Google’s strategy was perceived to increase the supply of AI labour (through a boost on its productivity) complementing those firms’ intangible AI-related investments. Interestingly, similar increases in value were not visible in firms whose workforces were at risk of automation. One interpretation is that they are expected to be disrupted by those firms developing AI systems and services.
Prasanna Tambe and co-authors also use LinkedIn skills data to estimate the value of intangible investments related to IT, finding that it is concentrated in a small group of ‘superstar firms’, and that it is associated with higher market value, suggesting that these investments are expected to generate important returns in the future. An important implication of this analysis is that the market expects the benefits from AI to be concentrated in a small number of firms, raising concerns about market power in tomorrow's AI-driven economy.
Meso view: sectoral differences in AI adoption and impacts
Think of a sector like health: the nature of the tasks undertaken in this industry, as well as the availability of data, the scope for changes in business processes and its industrial structure (including levels of competition and entrepreneurship) are completely different from, say, finance or advertising. This means that its rate of AI adoption and its impact will be very different from what happens in those industries. Previous editions of the Economics of AI conference included papers about the impact of AI in sectors such as media or healthcare. This year considered sector-specific issues in several areas, including R&D and regulation Sending machines to look for good ideas In the inaugural Economics of AI conference, Cockburn, Henderson and Stern proposed that AI is not just a General Purpose Technology, but also an ‘invention in the methods of invention’ that could greatly improve the productivity of scientific R&D, generating important spillovers for the sectors using that knowledge. One could even argue that the idea of the Singularity is an extreme case of this model where “AI systems that create better ideas” become better at creating “AI systems that create better ideas” in a recursive loop that could lead to exponential growth. This year, venture capitalist Steve Jurvetson and Abraham Heifts, CEO of Atomwise, a startup that uses AI in drug discovery spoke about how they are already pursuing some of these opportunities in their ventures, and two papers investigated the impact of AI on R&D: Our analysis of the deployment of AI in computer science research in arXiv, a pre-prints website popular with the AI community, supports the idea that AI is an invention in the methods of development: it has experienced rapid growth in absolute and relative terms, it is being adopted in many computer science subfields, and it is already creating important impacts (measured with citations) wherever it is adopted. AI is being adopted faster in fields such as computer vision, natural language processing, sound processing and information retrieval where there are big datasets to train machine learning algorithms, highlighting how AI R&D advances faster in those areas with complementary datasets.
Agrawal and co-authors develop a formal model of the impact of AI on the R&D process in scientific fields such as bio-medical and materials science, where innovation often involves finding useful needles in big data haystacks. An example of this is identifying which, among the millions of potential folds in a protein could be targeted by a pharmaceutical drug. AI systems trained on labelled data about previous successes and failures could help identify which of these combinations have the greatest potential, reducing waste and reviving sluggish productivity growth in R&D. Realising these benefits will require access to training data and building research teams that bring together AI skills with domain knowledge about the scientific fields where AI is being adopted. Sending bounty hunters to keep an eye on the machines Regulation sets the context and rules of the game that shape the rate and direction of new technologies such as AI. At the same time, regulation is itself an industry whose structure and processes are being transformed by AI systems that accelerate the pace and breadth of change, and create new opportunities to monitor economic activity. Two talks at the conference focused on this two-way street between regulation and AI. Suk Lee and co-authors have surveyed businesses about how they would change their AI adoption plans in response to different regulatory models. They show that general-purpose regulations would create more barriers to AI adoption than sector-specific regulations, and that regulation increases demand for managers to oversee AI adoption while reducing demand for technical and lower-skilled workers. It also creates bigger barriers for smaller firms, highlighting the trade-offs between AI regulation, innovation and competition.
Clark and Hadfield argue that the regulatory industry needs innovation to keep up with the fast pace of change in AI technologies, but public sector regulators lack flexibility and incentives to do this effectively. In order to remove this bottleneck, they propose the creation of regulatory markets where government licenses private sector companies to regulate AI adoption to achieve measurable outcomes (for example to lower AI error rates and accidents below an agreed threshold): this would give private sector firms the incentives and freedom to develop innovative regulatory technologies and business models, although it also raises the question of who would regulate these new regulators, and how to avoid their capture by the industries they are meant to regulate.
Micro view: Inside the AI adoption black box
Modern AI systems based on machine learning algorithms that detect patterns in data are often referred to as black boxes because the rationale for their predictions is difficult to explain and understand. Similarly, the firms adopting AI systems look like black boxes to economists adopting a macro view: AI intangibles are after all a broad category of business investments including experiments with various processes, practices and new businesses and organisational models. But what are these firms actually doing when they adopt an AI system, and what are the impacts? Several papers presented at the conference illustrated how economists are starting to open these organisational black boxes to measure the impact of AI and how it compares with the status quo. As they do this, they are also incorporating into the Economics of AI some of the complex factors that come into play when firms deploy AI systems that do not simply increase the supply of predictions, but also reshape the information environment where other actors (employees, consumers, competitors, the AI systems themselves) make decisions, leading to strategic behaviours and unintended consequences that are generally abstracted in the macro perspective. Susan Athey and co-authors compare the service quality of UberX and UberTaxi rides in Chicago. Their hypothesis that UberX drivers whose job depends on user reviews will provide higher quality rides is confirmed with an analysis of granular telematic data about driving speed and duration, number of hard brakes etc. They also test whether giving drivers information about their performance changes their behaviour, finding that the worst performers tend to improve their driving in response to these nudges. The paper shows that AI systems are an ‘invention in the methods for managing and regulating increasingly important digital platforms and marketplaces’, while also raising concerns about worker privacy and manipulation.
Michael Luca and co-authors (no link to the paper) test the effectiveness of various systems to select what Boston restaurants should be targeted with health inspections. They show that recommendations from a complex machine learning algorithm outperforms the rankings generated by human inspectors. Interestingly, they also detect high levels of inspector non-compliance with AI recommendations, suggesting that organisations using AI to inform their employees' decisions will have to overcome worker reticence and mistrust of these systems
Adair Morse and co-authors analyse the impact of ‘fintech’ AI systems in consumer-lending discrimination, finding that these systems tend to reduce - although not eliminate - discrimination against Latinx and African-American borrowers compared with face-to-face lenders, both in terms of the interest rates charged and the loan approval rates. AI systems still discriminate by identifying proxies for protected characteristics in the data. This shows how the adoption of AI can reduce old problems (human prejudice) while introducing new ones (algorithmic bias).
Meta view: Using AI to research AI
AI techniques have much to contribute to economics studies that often seek to detect (causal) patterns in data. Susan Athey surveyed these opportunities In the inaugural Economics of AI conference, with a particular focus on how machine learning can be used to enhance existing econometric methods. Several papers presented in this year’s conference explored new data sources and methods along these lines, for example using big datasets from LinkedIn and Uber to respectively measure technology adoption and service quality in car rides, and online experiments to test how UberX drivers react to informational nudges. In Nesta, we are analysing open datasets with machine learning methods to map AI research. Although these methods open up new analytical opportunities, they also raise challenges around reproducibility, particularly when the research relies on proprietary datasets that cannot be shared with other researchers (with the added risk of publication bias if data owners are able to control what findings are released), and ethics, for example around consent for participation in online experiments. In our own work we seek to address these issues by being very transparent with our analyses (for example, the data and code that we used in our AI mapping analysis is available in GitHub) and developing ethical guidelines to inform our data science work.
Prospective view: future avenues for the Economics of AI
Having summarised key themes and papers from the conference, I focus on some questions that I felt were missing. Recognising that to err is algorithm Macro studies of the impact of AI assume AI will increase productivity as long as businesses undertake the necessary complementary investments. They pay little attention to new issues created by AI such as algorithmic manipulation, bias and error, worker non-compliance with AI recommendations, or information asymmetries in AI markets, some of which are already being considered in micro studies from the trenches of AI adoption. These factors could reduce AI’s impact on productivity (making it mediocre and therefore predominantly labour displacing), increase the need to invest in new complements such as AI supervision and moderation, hinder trade in AI 'lemons' and have important distributional implications, for example through algorithmic discrimination of vulnerable groups. Macro research on AI should start to consider explicitly these complex aspects of AI adoption and impact, rather than hiding them in the black box of AI-complementing intangible investments and/or assuming that they are somehow exogenous to AI deployment. As an example, in previous work I started to sketch what such a model could look like if we take into account the risk of algorithmic error in different industries, and the investments in human supervision required to manage it. Modelling AI progress In general, the research presented at the Economics of AI conference modelled AI as an exogenous shock to the economy, in some cases explicitly as with Daniel Rock’s study of the impact of TensorFlow's release on firms' value. Yet AI progress is, itself, an economic process whose analysis should be part of the Economics of AI agenda. In his conference dinner speech, Jack Clark from OpenAI described key trends in AI R&D: we are witnessing an ‘industrialisation of AI’ as corporate labs, big datasets and large scale IT infrastructures become more important in AI research, and at the same time a ‘democratisation of AI’ as open source software, open data and cloud computing services make it easier to recombine and deploy state of the art AI systems. These changes have important implications for AI adoption and impacts. For example, the fact that researchers in academia increasingly need to collaborate with the private sector in order to access the computational infrastructure required to train state of the art AI systems could diminish spillovers from this research. Meanwhile, the diffusion of AI research through open channels creates significant challenges for regulators who need to monitor compliance in a context where adopting 'dual use' AI technologies is as simple as downloading and installing some software from a coding repository like GitHub. Few if any of the papers presented at the conference addressed these topics. Future work could fill these gaps by developing formal models of AI progress through an AI production function that takes inputs such as data, software, computational infrastructure and skilled labour to produce AI systems with a level of performance. In this paper, Miles Brundage started outlining qualitatively what that model could look like. This model could be operationalised using data from open and web sources and initiatives to measure AI progress from EEF and the Papers with Code project in order to study the structure, composition and productivity of the AI industry, and its supply of AI technologies and knowledge to other sectors. Recent work by Felten, Raj and Seamans where they use the EFF indicators to link advances in AI technologies with jobs at risk of automation illustrates how this kind of analysis could help forecast the economic impacts of AI progress and inform policy. Studying the direction of AI inventive activity Perhaps unsurprisingly given the point above, most of the research presented at the conference adopted a 'monolithic' definition of AI that equates it with the deep learning techniques currently dominating the field. This neglects concerns about the lack of robustness, explainability, data efficiency and environmental sustainability of deep learning algorithms, and the fact that alternative AI research and technological trajectories could be feasible and perhaps desirable. However, as Daron Acemoglu showed some time ago, the market will undersupply alternatives to a dominant technology if researchers are not able to capture the benefits of sustaining technological diversity. Acemoglu pointed out that maintaining diversity in researchers’ capabilities and beliefs and providing public funding for less commercially oriented alternatives are two potential strategies to bring levels of technological diversity closer to what is socially optimal. Could lack of technological diversity become a problem in the AI field? Lack of diversity in the AI research workforce, and the increasing influence of the private sector in AI research agendas through the aforementioned industrialisation of AI research give reason for concern but the evidence base is lacking. More research is needed to measure AI's technological diversity and how it is shaped by the goals, preferences and agendas of the scientists, engineers and organisations involved in it. This is an active area of research at Nesta where we will be publishing some findings soon. Remembering the political economy of AI In the inaugural Economics of AI conference, Tratjenberg and Korinek and Stiglitz asked who will benefit and who will suffer when AI arrives, whether AI deployment could become politically unacceptable, and what policies should be put in place to minimise the societal costs of AI when it is deployed. More recently, Daron Acemoglu and Pascual Restrepo expressed concerns that the AI industry might be building ‘the wrong kind of AI’ because it does not internalise negative externalities from AI deployment (eg. labour market disruption) and because some of its leaders are biased in favour of mass automation regardless of its risks. These important questions were largely absent from the debate in Toronto, yet economists need to formalise and operationalise models of the distributional impacts of AI and its externalities in order to inform policies to ensure that its economic benefits are widely shared and reduce the risk of a public backlash against AI.
Conclusion: Think Internet, not Skynet
| 2019-10-07T00:00:00 |
https://www.nesta.org.uk/blog/economics-artificial-intelligence-today/
|
[
{
"date": "2019/10/07",
"position": 98,
"query": "AI economic disruption"
}
] |
|
Robotics and the Future of Production and Work | ITIF
|
Robotics and the Future of Production and Work
|
https://itif.org
|
[
"Robert D. Atkinson"
] |
They estimated the number of U.S. jobs lost due to robots since 1990 is somewhere between 360,000 and 670,000—quite a small number in an economy ...
|
Introduction
The Need for Faster Productivity Growth
The Productivity Potential of the Next Production System
Patterns of National Robot Adoption
Why Do Some Countries Lead in Robot Adoption?
Global Supply Chains and Reshoring?
Robots and Jobs
Robots, Wages, and Inequality
Conclusion
Endnotes
Companies around the world are increasing their use of robots. According to the International Federation of Robotics (IFR), the global average for industrial robots per 10,000 manufacturing workers grew from 66 in 2015 to 85 in 2017.[1] With integration of artificial intelligence and other improvements in robotics (e.g., better machine vision, better sensors, etc.), robotics promises to see significantly improved pricing and performance over the next decade. As a potentially new general-purpose technology, a central question is whether and how robotics will impact production processes, particularly in such globally traded sectors as manufacturing. The last major technology wave, driven by information technology, was largely decentralizing in nature, enabling the geographic distribution of far-flung supply chains to the periphery in search of cheap labor. Will the next wave of technology innovation based on robotics have the opposite effect, enabling a reshoring of manufacturing to the core? This paper examines the nature and prospects of robotics and associated production technologies, reviews the literature on their impact on spatial dynamics, reviews recent data on robotic adoption, including controlling for robot adoption rates by domestic worker compensation rates, and speculates on future trends in the spatial distribution of manufacturing.
There is both considerable excitement and trepidation about the so-called “fourth industrial revolution” and its ability to power growth around the world. (This paper eschews the term “fourth industrial revolution,” because it is a misleading and overly simplistic term—if anything, there have been at least six major production technology systems since the late 1700s, not four. The more accurate term is the “next production system.”)
While there are many important questions about the next production system, including the timing of impacts, the nature of the technologies involved, and the effects on industries, labor markets, and productivity, one critical question is how its impacts will likely differ between developed and developing economies. The short answer is that while both developed and developing economies will benefit from the next production system, developing economies will likely benefit less, in part because their lower labor costs provide less incentive to replace it with technology, and because the new production systems appear to enable shorter production runs, smaller factories, and higher productivity—all of which should enable reshoring to higher-wage nations.
As the next wave of technological innovation emerges, interest in technology’s role in international affairs appears to be growing.[2] But much of that focus is on product technology (e.g., smartphones, commercial jets, automobiles, solar panels, etc.) rather than on process technology (“machines” to improve how a good or service is produced) that enables automation.
While both developed and developing economies will benefit from the next production system, developing economies will likely benefit less.
Automation is a particular kind of process technology. The term “automation” was originally coined in 1945 when the engineering division of Ford Motor Company used it to describe the operations of its new transfer machines that mechanically unloaded stamping from body presses and positioned them in front of machine tools. Today, it refers to any production process that is controlled by a machine, with little or no input from an operator in order to produce, in a highly automatic way. There are many technologies that can enable a production process to be automated, and robotics is an increasingly important one. While there is no hard and fast definition of “robotics,” the term generally refers to physical machines that can be programmed to perform a variety of different tasks, with some level of interaction with the environment, and limited or no input from an operator.
Robots are key tools for boosting productivity. To date, most robot adoption has occurred in manufacturing, wherein they perform a wide variety of manual tasks more efficiently and consistently than humans. But with continued innovation, robot use is spreading to other sectors, from agriculture to logistics to hospitality. Robots are getting cheaper, more flexible, and more autonomous, in part by incorporating artificial intelligence. Some robots substitute for human workers; others—collaborative robots, or “cobots,” which work alongside workers—complement them. As this trend continues, robot adoption will likely be a key determinant of productivity growth and will potentially reshape global supply chains.
The global economy is in need of a technology “shot in the arm”—of the kind the world experienced in the 1950s and early 1960s with electromechanical and materials innovations (steel, chemicals, plastics, etc.), and again in the 1990s with ICT innovations (personal computing, the Internet, broadband, etc.). Indeed, the global economy is in a productivity slump. The Conference Board found that change in gross domestic product (GDP) per person employed has slowed from 2.6 percent per year from 1999 to 2006 to around 2 percent per year from 2012 to 2014.[3] Most of this decline has occurred in developed economies: Productivity growth in the EU, Japan, and the United States fell by more than half after 2007, compared with the period from 1999 to 2006. And from 2005 to 2015, the world’s poorest nations (with gross national income per capita of less than $9,000 seeing labor productivity growth of just around 3 percent annually, a relatively low rate given productivity catch-up is easier for lagging economies).
Faster productivity growth in many functions and industries that involve moving or transforming physical things will be spurred by better and cheaper robots. Robots are already driving productivity.[4] Investment in robots contributed to 10 percent of GDP growth per capita in Organization for Economic Cooperation and Development (OECD) countries from 1993 to 2016, and there is a 0.42 correlation between a country’s wage-adjusted manufacturing robot adoption (see below) and growth in productivity between 2010 and 2017.[5]
Graetz and Michaels found that robot densification increased annual growth of GDP and labor productivity between 1993 and 2007 by about 0.37 and 0.36 percentage points respectively across 17 countries studied, representing 10 percent of total GDP growth—compared with the 0.35 percentage point estimated total contribution of steam technology to British annual labor productivity growth between 1850 and 1910.[6] A subsequent study by them found that investment in robots contributed 10 percent of growth in GDP per capita in OECD countries from 1993 to 2016.[7] The same study found that a one-unit increase in robotics density (which the study defines as the number of robots per million hours worked) is associated with a 0.04 percent increase in labor productivity. A study by the Institute for Employment Research found that robot adoption led to a GDP increase in Germany of 0.5 percent per person per robot over 10 years from 2004 to 2014.[8] Koch, Manuylov, and Smolka found that the introduction of industrial robots in Spanish manufacturing firms boosted output by 20 to 25 percent within four years, and reduced labor-cost share by approximately 6 percent.[9]
As robots and other autonomous systems continue to improve in functionality and decline in costs going forward, their likely impact on productivity will be even more significant. At least six technologies look like candidates to comprise the next innovation wave: the Internet of Things, advanced robotics, blockchain, new materials, autonomous devices, and artificial intelligence. Perhaps artificial intelligence and robotics are the most important. Artificial intelligence has many functions, including but not limited to learning, understanding, reasoning, and interaction.[10] And easy-to-program, dexterous, and relatively affordable robots could enable automation of a range of functions in agriculture, manufacturing, and services.
While these technologies are already in the marketplace, all are generally too expensive and ineffective to be widely adopted enough to drive higher rates of economy-wide productivity growth. This is why, for example, despite the excitement over “Industry 4.0” technologies, they do not appear to have been adopted on a large scale, as evidenced in part by most manufacturers in developed nations appearing to be in the very early stages of adoption.[11] Likewise, while there is considerable excitement about machine learning software systems, their current capabilities remain relatively limited—notwithstanding some promising early applications. Fully autonomous cars that are safe and sold at a price point most consumers can afford are likely at least 15 years away.[12] And fully dexterous robotic hands are not likely to be in the market before 2030, or even 2040.[13] As MIT roboticist Rodney Brooks wrote, “Having ideas is easy. Turning them into reality is hard. Turning them into being deployed at scale is even harder.”[14] If these technologies really were “ready for prime time,” one would expect to see higher rates of productivity growth. But, to paraphrase Robert Solow, we see the next production system everywhere except in the productivity statistics.
Even with these challenges, these next-production-system technologies are being developed and, in a growing array of cases, are already in use. One of these is robotics. As such, a critical question is how nations compare in robot adoption. The most commonly used metric is the number of industrial robots as a share of manufacturing workers. According to IFR, the global average for industrial robots per 10,000 manufacturing workers grew from 66 in 2015 to 85 in 2017.[15] South Korea was the world’s most advanced adopter with 710 robots per 10,000 workers; Singapore, Germany, Japan, and Sweden followed. The United States ranked seventh with 200 industrial robots per 10,000 workers. Russia and India ranked last with just 4 and 3 robots per 10,000 workers, respectively. (See figure 1.)
There is a stronger economic case for adopting robots in higher-wage economies than in lower-wage economies because investments in robots are often justified by how much they save in labor costs. This is why the Boston Consulting Group (BCG) estimated labor cost savings from robotics are considerably lower for developing nations.[16] So, the more germane question is: Where do nations stand in robot adoption when taking wage levels into account? [17] To assess this, the estimated time of payback (in months) from installing a robot must be calculated.[18]
Comparing the ranking of expected robot adoption given differences in compensation levels to actual rates, several patterns emerge. First, East Asian nations lead, occupying six of the top seven positions in the ranking: Korea leads with 2.4 times more robots adopted than expected, while Singapore, China, Thailand, and Taiwan follow. Japan ranks seventh. In contrast, Commonwealth nations lag behind significantly, with Canada ranking 14th (44 percent below expected adoption rates), the United Kingdom 23rd (73 percent below), and Australia 24th (80 percent below). (See figure 2.)
Overall, Europe is a laggard, with only two Eastern European countries adopting more than expected given its wage levels: Slovenia (37 percent above expected adopted rate) and the Czech Republic (25 percent above). All other EU nations had lower-than-expected adoption rates.
Among developing counties, Thailand leads with adoption rates 159 percent more than what its wage levels would predict, while China’s adjusted rate is 153 percent higher, up from 104 percent greater in 2016. Mexico also outperforms, with adoption rates 16 percent higher than expected. But Brazil, India, and Russia, even with their low wages, are laggards. India’s adoption is 66 percent below the expected rate, Brazil’s is 83 percent below, and Russia’s 88 percent below. Finally, the United States is significantly behind, ranking 16th, with adoption rates 49 percent below expected.
It is not clear why some countries lead and others lag. Wage levels are not the only factor. Robot adoption differs by industry, with the automobile industry generating the largest demand. Depending on the country, the industry accounts for 30 to 60 percent of total robot adoption. Yet many of the lagging nations—including Brazil, Canada, France, Germany, Italy, Russia, Spain, Sweden, and the United States—have robust automobile industries relative to the size of their manufacturing economies.[20] And China scores well in overall robot adoption despite having a relatively small automotive sector (on a per-GDP basis) compared with the rest of these nations.
Acemoglu and Restrepo found a modestly positive correlation between robot adoption and higher ratios of middle-aged workers, with the logic being that less robot adoption reflects a relative scarcity of middle-aged workers—who tend to have higher wages and often can be replaced by robots.[21] But the correlation is not strong enough to explain the large differences, even with the wage factor included in the analysis.
Cultural attitudes may play a role. Lee and Sabanovic found that cultural attitudes play a role in robot adoption rates, with South Koreans having more favorable views of robots in the economy than Americans.[22] Some countries appear to welcome robots—Japan even has an annual “Robot Award”—while others embrace narratives of Terminator-like machines destroying jobs.[23] There is a modest positive correlation of 0.20 between the countries’ wage-adjusted industrial robot adoption rates and the degrees to which countries’ residents believe more emphasis should be placed on the technology in the future.[24]
Industrial relations may also play a role. For example, some argue that one reason South Korea is so far ahead is its industrial unions are quite militant, engaging in strikes and other work stoppages on a fairly regular basis, particularly in the auto industry.[25] In response, many of the “chaebols” (large, usually family owned, business conglomerates) have turned to robotics as a way to ensure more production stability.
Government policies also appear to play a key role. Some of the leading countries have established national strategies to support robotics innovation and adoption. In 2014, Japan established a goal to realize a “new industrial revolution driven by robots,” while South Korea enacted its Intelligent Robot Development and Promotion Act.[26] Japan has also established public-private robotics research and development (R&D) partnerships, which one study found were highly effective in spurring robot development.[27] In contrast, the United States lacks a national robotics strategy.
China appears to be in a class of its own, with its national and provincial governments committing massive amounts of money toward subsidizing robotics adoption.
Some of the leaders, particularly South Korea, Taiwan, and Japan, also have robust public programs to help manufacturers—particularly small and medium-sized enterprises—adopt advanced technologies, and some nations have proactive tax policies to provide incentives for advanced technology adoption, including robotics.[28] In Singapore, for example, firms can expense in the first year all investments in computers and prescribed automation equipment, robots, and energy-efficiency equipment.[29] South Korea provides an investment tax credit for new equipment, while Japan and Slovenia provide accelerated depreciation on new equipment.[30] In contrast, some nations, such as the United States and United Kingdom, have less generous tax treatment of capital expenditures and exhibit lower levels of capital expenditures by manufacturers.[31]
China appears to be in a class of its own, with its national and provincial governments committing massive amounts of money toward subsidizing robotics adoption. China’s Robotics Industry Development Plan (2016–2020), part of its Made in China 2025 initiative, promotes domestic robot production and sets a goal of expanding robot use by such companies tenfold by 2025. As a result, many provincial governments are providing generous subsidies for firms to buy robots—although the accuracy of reported figures is potentially dubious, largely because the numbers are so high and provincial governments have strong incentives to inflate reported numbers in order to gain favor with the national government. Guangdong province will supposedly invest 943 billion yuan (approximately $135 billion) to help firms carry out “machine substitution.” Likewise, the provincial government of Anhui has stated it will invest 600 billion yuan (approximately $86 billion) to subsidize industrial upgrading of manufacturers in its province, including through robotics.[32] Nonetheless, China appears to provide greater subsidies for robot adoption than any other nation. As a result, if China’s and South Korea’s respective growth rates continue at the same pace achieved between 2016 and 2017, then by 2026 China will lead the world with the highest number of industrial robots as a share of its industrial workers.
Past major waves of technological innovations have had different spatial impacts, favoring some nations more than others. The next production system will likely be no different and will play out in two areas: productivity and international competitiveness.
Over the last 40 years, improvements in global transportation and information technology have enabled significant offshoring of supply chains to low-wage countries. And even though the productivity of workers in low-wage countries is lower than in higher-wage countries for many industries and functions, the low wages more than compensate for lower productivity and increased transportation costs. This process began with the well-documented offshoring of low-technology, low-value-added, labor-intensive manufacturing industries such as textiles, apparel, and luggage to East Asian and Latin American countries starting in the mid-1970s. And the trend has continued. Imports of wood furniture, for example, increased from 38 percent in 2000 to 68 percent of the U.S. market in 2008.44 Today, American producers account for just 1 percent of the U.S. luggage market and 1.7 percent of the outerwear apparel market.
This may change as automation technology, including robotics—which is available anywhere in the world—improves and allows more work in advanced countries to be automated. So why won’t low-wage countries install it at the same rates as higher-wage countries? The answer is, absent government subsidies, it makes less economic sense to install robots in these locales. For example, assuming a $250,000 initial investment in a robot that replaces two workers (one on each shift) in the United States, where annual total compensation for the average manufacturing worker is $72,000, the payback period (the time it takes for savings to exceed costs) would be less than one year.[33] But in Mexico, where the average compensation is $14,000, the payback is much longer: eight years and four months. And in the Philippines, where average compensation is just $4,200, payback is longer than 30 years. Given that most firms require paybacks of less than four or five years, this suggests a very slow rate of robot penetration in low-wage developing nations. This is why BCG estimated the labor cost savings from robotics to be considerably lower for developing nations.[34]
However, robot costs are declining and performance is improving. Will this make a difference? The Boston Consulting Group predicted a percent reduction in prices and a 5 percent improvement in performance in robotics per year over the next decade.[35] If robotic innovation advances rapidly, to where the cost of a robot falls to about $50,000, paybacks in emerging markets will begin to make more economic sense. In Mexico, that period is one year and nine months. But in the Philippines, the payback is still long: eight years and four months. Moreover, such improvements may not be realized.[36] This suggests lower-wage nations will lag in their ability to take advantage of these technologies. This trend could widen productivity and income differences with developed nations.
This is why it is likely higher-wage nations will get more of a productivity boost from these technologies than lower-wage ones. In its estimates of the impact of labor displacement by automation between now and 2030, the McKinsey Global Institute found that higher income nations will have higher rates of labor displacement because the higher wages make it more economical to invest in labor-replacing technology.[37] While installing some of these technologies will be less expensive in lower income nations, the relative price of the technology compared with labor costs will still be higher than in higher-wage nations. As such, the payback time for the investments in terms of labor savings will be considerably longer in lower-wage nations.
If robotic innovation advances rapidly, to where the cost of a robot falls to about $50,000, paybacks in emerging markets will begin to make more economic sense.
This could mean long-standing centrifugal forces, in which commoditized production has spun out of rich nations to low-cost nations, could slow—or even reverse—thereby generating centripetal forces wherein at least some work comes back to serve local markets. In manufacturing, smart manufacturing systems will enable more flexible production and shorter production runs. The application of information and communication technology to every facet of manufacturing is reshaping modern manufacturing. Smart manufacturing is being driven by many technologies, including computer aided design software, cloud computing, the Internet of Things, sensor technologies, 3D printing, robotics, data analytics, machine learning, and wireless connectivity. This digitalization is changing how products are designed, fabricated, operated, and serviced, just as it is transforming the operations and processes of manufacturing supply chains.
In other words, current manufacturing systems largely enable either high-volume, low-mix output (e.g., producing large quantities of the same unit; mass production) or low-volume, high-mix output (e.g., producing smaller quantities of different units; batch production). The latter are often located in lower-wage countries. But convergence of digital technologies and manufacturing increasingly leads to a new production paradigm: a high-volume, high-mix approach that enables cost-efficient production in smaller factories more evenly distributed around the globe to serve local markets. Indeed, Rauch, Dallasega, and Matt, engineering professors at the Free University of Bozen-Bolzano, have argued that these emerging technologies will enable more decentralized and geographically dispersed manufacturing systems.[38] In a survey of 238 Citigroup clients, 70 percent believed automation would encourage companies to consolidate production and move their manufacturing closer to home.[39] Krenz, Prettner, and Strulik estimated that, within manufacturing sectors, an increase by 1 robot per 1,000 workers is associated with a 3.5 percent increase in reshoring activities.[40] And an OECD report finds that, to date, robotics slows down—and in some cases, stops—offshoring and is thus a key to helping keep manufacturing in developed economies.[41]
What about job loss? There has been considerable ink spilled warning of the coming job-destruction tsunami from the next production system. A widely cited study by Oxford University researchers Carl Benedikt Frey and Michael A. Osborne set the tone in 2013 when it claimed that 47 percent of U.S. employment was at risk of job loss from new technology.[42] Yet, these and similar studies warning the next production system will lead to massive job loss and potentially high levels of structural unemployment suffer from a number of mistakes.
First, these studies assume we are heading to a transformative fourth industrial revolution the likes of which the world has never seen, leading to rapid productivity growth. Berg, Buffie, and Zanna reflected this view when they wrote, “The premise of this paper is that we are in the midst of a technological inflection point, a new ‘machine age’ in which artificial intelligence and robots are rapidly developing the capacity to do the cognitive as well as physical work of large fractions of the labor force.”[43] The McKinsey Global Institute estimated that, compared with the Industrial Revolution of the late 18th and early 19th centuries, artificial intelligence’s disruption of society is happening 10-times faster and at 300 times the scale—which means roughly 3,000 times the impact.[44]
There are two main problems with such speculations. First, they are just that: grounded in little evidence and completely unbound from historical analysis. Moreover, many estimates of exponential growth, such as the McKinsey estimate, refer to adoption rates of particular technologies, such as mobile phones, to extrapolate to overall rates of technological innovation and productivity growth rates. Moreover, there is no evidence provided that the societal pace of change for technology is 10-times faster now than two centuries ago, much less faster at all. These are all premised on adoption rates of technologies such as mobile phones and Internet adoption. But what about the much slower adoption rates of other information technologies such as digital signatures and biometrics? In fact, Bloom, Jones, Van Reenen, and Webb found the productivity of R&D has been declining, thereby making it harder to get innovation.[45]
Second, many studies look only at the impact of robots on jobs in the region adopting them, and not surprisingly, usually find that regions with higher robot adoption have either declining employment growth or slower-than-economy-wide employment growth. For example, Chiacchio, Petropoulos, and Pichler have studied the impact of industrial robots on employment in 116 regions in six EU-15 nations and found that regions with a faster rate of robot adoption had lower rates of labor force growth.[46] But this is not surprising, as regions that specialize in manufacturing will likely experience slower employment growth if manufacturing productivity grows faster than non-manufacturing productivity. The relevant question is, does higher productivity in an overall economy lead to lower employment growth? There was, in fact, a correlation of 0.15 between productivity growth and total growth in labor hours in EU-15 nations from 1997 to 2015, suggesting productivity does not have negative consequences for employment growth.[47]
Acemoglu and Restrepo focus on local labor markets in the United States, but have also attempted to measure the impacts of industrial robots on all labor markets.[48] They found that robot adoption leads to fewer net jobs as expected. However, its impacts are quite small. They estimated the number of U.S. jobs lost due to robots since 1990 is somewhere between 360,000 and 670,000—quite a small number in an economy with over 130 million jobs. Moreover, when the researchers included a measure of the change in computer usage at work, they found a positive effect.
Moreover, a number of other studies find no evidence for job loss. In an analysis of industrial robots on employment in German labor markets between 1994 and 2014, Dauth, Findeisen, Suedekum, and Woessner found that the adoption of industrial robots had no effect on total employment in local labor markets specializing in industries with high robot usage.[49] In an analysis of the impact of automation on jobs in Europe, Gregory, Salomons, and Zierahn found that while technology-based automation displaces jobs, “it has simultaneously created new jobs through increased product demand, outweighing displacement effects and resulting in net employment growth.”[50] As discussed, Koch, Manuylov, and Smolka found that adoption of robots in manufacturing firms in Spain has led to net job creation of about 10 percent.[51]
It is likely the emergence of the next production system and improvement in robotics technology will increase both productivity and labor-market churn. But higher labor-market-churn rates are not the same as higher unemployment rates.
Firm-level studies that show job loss from robots find results that are opposite from virtually all the studies that have examined this at the macroeconomic level, which find that productivity growth has no negative effect on employment, at least in the moderate term. There are a number of reasons why job impacts, even at the industry level, are likely to be minimal. Mayer found a higher share of robots helps economies’ manufacturing sectors gain global market share.[52] Because of this gain, the correlation between robot use and manufacturing as a share of national employment is negative, albeit only slightly.[53] Conversely, it is countries such as Canada, the United States, and the United Kingdom—those with low rates of manufacturing adoption and automation—that have seen the highest rates of manufacturing job loss over the past two decades.[54] There are three reasons countries can lose manufacturing employment: slower growth in manufacturing consumption relative to non-manufacturing consumption, higher manufacturing productivity growth relative to non-manufacturing, and reduced output from loss of international competitiveness (e.g., manufacturing exports growing slowly or declining while imports grow). In the U.S. case, Information Technology and Innovation Foundation (ITIF) estimated that over half of the very steep loss of manufacturing jobs between 2000 and 2011 (over 33 percent) was caused by trade (manufacturing imports increasing faster than exports), and less than half by faster manufacturing productivity.[55]
Second, companies invest in process innovations to cut costs (and sometimes to improve quality). They pass a significant share of those savings to consumers in the form of lower prices (with some going to workers in the form of higher wages and others to shareholders via higher profits). But the savings are not buried, they are recycled—and this added purchasing power is spent or invested, thereby creating new jobs. This is why OECD has found, “Historically, the income-generating effects of new technologies have proved more powerful than the labor-displacing effects: technological progress has been accompanied not only by higher output and productivity, but also by higher overall employment.”[56] Likewise, in a study of 24 OECD nations, Tang found that, “at the aggregate level there is no evidence of a negative relationship between employment growth and labour productivity growth.”[57] Likewise, in its 2004 World Employment Report, the International Labor Organization found strong support for simultaneous growth in productivity and employment in the medium term.[58] Van Ark, Frankema, and Duteweerd also found strong support for simultaneous growth in per-capita income, productivity, and employment in the medium term.[59]
Third, many of the studies looking at the impacts of technology on jobs significantly overstate the likelihood of job loss from new technology, in part because they focus on jobs rather than discrete tasks. Some tasks might be automatable, but the overall job might not be. For example, Arntz, Gregory, and Zierahn have argued the Oxford study overstates that share of automatable jobs by “neglecting the substantial heterogeneity of tasks within occupations as well as the adaptability of jobs in the digital transformation.” They found that when controlling for these factors, the automation risks of U.S. jobs drops from 38 percent to 9 percent.[60]
To be sure, it is likely the emergence of the next production system and improvement in robotics technology will increase both productivity and labor-market churn, as more workers are likely to lose their jobs due to technological displacement.[61] But higher labor-market-churn rates are not the same as higher unemployment rates because, historically, higher churn rates are not associated with higher unemployment rates. For example, in the 1990s, the labor market churn rates (the share of workers losing their jobs due to establishments closing or downsizing) was about 25 percent higher than in the prior decade, but overall unemployment was low.[62]
Higher levels of churn only lead to higher levels of unemployment if the dislocated workers do not reenter the labor market in a timely manner.
Even if there is little reason to believe there will be significantly higher rates of structural employment from the next production system, a number of scholars have argued that it will lead to increased income inequality and possible immiseration for many workers. But these
studies suffer from significant methodological and logical flaws, thereby rendering their conclusions flawed.
A leading example of this work is the report by Berg, Buffie, and Zanna, “Should We Fear the Robot Revolution? (The Correct Answer is Yes).” Their finding is a bit surprising given that, in a prior article for the International Monetary Fund’s Finance & Development Journal, they stated that “technology does not seem to be the culprit for the rise in inequality in many countries [which is] concentrated in a very small fraction of the population.”[63] Perhaps they think this time will be different. Their study, however, is a prime example of Kenneth Boulding’s famous quote that while mathematics brought rigor to economics, and it also brought mortis.[64] The authors created “four models of the short and long-run effects of robots on output and its distribution in a family of dynamic general equilibrium models.” They found that in all four models, robots increase productivity but reduce wages. But the assumptions of models is unrealistic. For example, their first model had robots capable of doing all jobs, something that even the most enthusiastic believer in the power of the next production system would argue is unrealistic.
Overall, this and related studies make three major methodological errors and logical mistakes. The first is they do not adequately account for second-order effects and the fact that when organizations use robotics to automate and eliminate work, they do so to reduce costs. Acemoglu and Restrepo wrote that automation technologies “reduce overall labor demand because they are displacing workers from the tasks they were previously performing.”[65] Even when this is true, few if any organizations spend more on robots than they save in labor costs (unless they are using robots to boost quality). And those labor-savings costs are not buried. They are spent—and that spending creates jobs. This is why, as ITIF found, from 1850 to 2015, despite some decades with significant occupational churn from automation technology (e.g., the tractor, automatic elevator, automatic telephone switch, etc.), employment grew at the same rate as the labor force.[66] As Autor wrote, “Automation does indeed substitute for labor—as it is typically intended to do. However, automation also complements labor, raises output in ways that lead to a higher demand for labor, and interacts with adjustments in labor supply. Even expert commentators tend to overstate the machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings and augment demand for labor.”[67]
In some of the models, researchers accept that there are savings but then assume that the lion’s share of the savings are captured by “capital” and few go to labor either in the form of higher wages or lower prices. But this is illogical, and history suggests it is wrong. The only way capitalists can capture the majority of the gains from automation is if limited competition in the market allows them to capture most or all of the savings as profits. If this is true, then why over the last 40 years, when labor productivity has more than doubled, are corporate profits essentially unchanged? The answer is competitive markets limit the ability of companies to capture most of the gains from productivity as profits, especially over the medium to long term. Moreover, no one has made a convincing case that there is anything about the next production system that would lead to massive monopolization of the global economy in virtually all sectors. Competition, especially backed up by national antitrust authorities, is not likely to die.
In some models, researchers accept that there are savings but then assume that the lion’s share of the savings are captured by “capital.” This is illogical, and history suggests it is wrong.
Second, Berg, Buffie, and Zanna only looked at first-order effects, so their models find that unemployment goes up as automation makes tasks more efficient. Their models then determine the wage rate on the basis of supply and demand, which leads to the illogical finding that increased labor output (which all four of their models find) leads to decreased labor income and a larger share of income going to capital. Because they focused on allocation efficiency, rather than on productive efficiency, they assumed less demand for labor with the same supply, and therefore that the price of labor must fall. The wrote, “At first, the real wage is likely to fall in absolute terms, even as the economy grows.”[68]
There are several things wrong with this framing. First, the supply of labor does not fall once second-order effects are taken into account. In other words, productivity leads to lower prices, which leads to increased demand and therefore restores labor demand. Second, it is vast oversimplification to suggest the real price of labor is a function solely, or even principally, of the relationship between supply and demand of labor. If the Keynesian revolution told us one thing it was that the classical-economics view that labor prices are a function of supply is wrong; wage rates are in fact sticky, which is why, for example, wages generally to do not fall during recessions. Institutional factors such as the minimum wage, employer-labor contracts, unionization, and the need for companies to maintain the goodwill of their workers, all mean that even if unemployment rates were to go up from technology-based automation (which is not likely to happen, at least during non-recessionary periods), wage rates would not fall. Therefore, as the U.S. Bureau of Labor Statistics has found, when firms reduce costs through automation, those savings raise wages or lower prices, or both.[69] Likewise, Graetz, and Michaels, in a review of the economic impact of industrial robots across 17 countries, found that robots increase wages while having no significant effect on total hours worked.[70]
Finally, many of the claims that the next production system will boost inequality point to the decline in labor’s share of national income in the United States as evidence that technology has harmed labor and helped capital—and that this decline will accelerate going forward. But this view reflects a serious misreading of national income accounts. First, when looked at over the longer term, and when using net income instead of gross, there has been almost no decline in the share of U.S. national income going to labor. Gross domestic income (GDI) includes depreciation (what the U.S. Bureau of Economic Analysis terms “capital consumption”), which amounts to about 16 percent of GDI. It also includes business taxes, which are around 7 percent of GDI. When these are pulled out, labor’s share of net income was around 70 percent of net domestic income in 2017. In 1949, this share was 69 percent.[71] It is true that labor’s share rose slightly from 1940 to the early 1990s to around 73 percent and has fallen slightly since then. But that decline was not mostly from the rise of corporate profits, but rather from the rise of housing income and proprietor income. When looking at GDI, the share of labor fell by 2.6 percentage points from 1985 to 2017. But the share going to net interest and corporate profits actually declined. So, where did the income go? The share of GDI going to rental income increased 3.1 percentage points, while consumption of fixed capital increased by 1 percentage point. In other words, the fall in the share of labor income had nothing to do with capital becoming more important than labor. It had more to do with housing becoming more important than labor, with the demographic forces pushing up demand for housing, and government zoning rules limiting supply.
Many of the claims that the next production system will boost inequality point to the decline in labor’s share of national income as evidence that technology has harmed labor and helped capital—and that this decline will accelerate. But this view reflects a serious misreading of national income accounts.
These models hypothesize a growing inequality between capital and labor. Some argue instead that the major growth on inequality from robots will be within labor. It appears the automation impacts from the next production system will be significantly larger for lower-wage and lower-skill occupations. To assess this, the risks of automation by occupation were compared to occupational wage levels and years of schooling needed for the occupation using two data sets: the Oxford study by Osborne and Frey, and a study by ITIF. The correlation between the average wage of an occupation and its risk of automation is negative and quite large for both data sets (-0.59 for Oxford, -0.52 for ITIF). The correlation of average years of schooling and risk of automation is also negative and large (-0.64 for Oxford, -0.51 for ITIF).[72] Similarly, the White House Council of Economic Advisors also used the Oxford data and found 83 percent of jobs making less than $20 per hour would come under pressure from automation, as compared with 31 percent of jobs making between $20 and $40 per hour, and just 4 percent of jobs making above $40 per hour.[73] This is not a reflection of the actual wages of the jobs (in fact, the incentive to automate jobs is greater the higher the wage level.) Rather, it refers to the kinds of jobs and tasks that are most amenable to automation (routine, low-productivity jobs that pay poorly). OECD estimated 44 percent of American workers with less than a high-school degree hold jobs made up of highly automatable tasks, while only 1 percent of people with a bachelor’s degree or higher hold such a job.[74]
Many will argue that these future occupational automation patterns are problematic, and cause individuals with lower incomes to be more at risk. While true, if this occupational impact pattern occurs, the occupational profile of advanced economies will by definition shift to one with a higher share of middle- and upper-wage jobs (as lower-wage jobs are automated at higher rates and therefore employ fewer people). This would result in relatively fewer lower-paying jobs and more higher-wage jobs—a plus for many workers now employed in occupations whose wages remain low and stagnant. The reason behind employment shifting to more middle- and higher-wage jobs is not necessarily intuitive. As more lower-wage jobs become automated, the prices of the goods and services still produced by the lower-wage workers also declines in relative terms (were there no associated cost savings, firms would have no incentive to employ technology to boost productivity). These savings result in consumers across the income spectrum spending more on other goods and services—with the employment generated by this added production in industries with low-, middle-, and high-wage jobs. Thus, added demand creates more middle- and higher-wage jobs.
Moreover, the fact that many workers in low-wage jobs are overqualified suggests that at least some workers now holding these jobs have enough skills to move relatively easily into higher paying, moderately-skilled jobs.[75] In most developed nations, there is a modest share of workers with college degrees who are employed in jobs that do not require one. Although some are in these occupations by choice, many others settle for these positions because there are simply not enough available jobs that require a college education. On average, these workers should have an easier time transitioning to newly created middle-wage jobs than workers with less education and skills. To be sure, this doesn’t mean it will be easy for all dislocated workers to transition to better jobs. For them, there is an urgent need to improve policies and programs to boost skills, especially of workers in low-wage jobs.
The next production system will be a welcome development for a global economy that is experiencing lagging investment and productivity growth. This next technology wave holds the potential to lead to a virtuous cycle of increased investment, faster rates of productivity and wage growth, and more spending. It appears likely that developed nations will benefit more, both from higher rates of investment and productivity growth, and from production systems that are more conducive to localized production. Moreover, notwithstanding some studies that suggest the next production system will lead to higher structural unemployment and reduced labor incomes, the evidence and logic suggest structural unemployment will not increase, and labor will receive a significant share of the benefits (akin to historical shares). Policymakers should therefore support—not resist—the development of the next production system.
About the Author
Robert D. Atkinson is the founder and president of ITIF. Atkinson’s books include Big Is Beautiful: Debunking the Myth of Small Business (MIT, 2018), Innovation Economics: The Race for Global Advantage (Yale, 2012), and The Past and Future of America’s Economy: Long Waves of Innovation That Power Cycles of Growth (Edward Elgar, 2005). Atkinson holds a Ph.D. in city and regional planning from the University of North Carolina, Chapel Hill, and a master’s degree in urban and regional planning from the University of Oregon.
About ITIF
The Information Technology and Innovation Foundation (ITIF) is a nonprofit, nonpartisan research and educational institute focusing on the intersection of technological innovation and public policy. Recognized as the world’s leading science and technology think tank, ITIF’s mission is to formulate and promote policy solutions that accelerate innovation and boost productivity to spur growth, opportunity, and progress.
For more information, visit us at www.itif.org.
| 2019-10-15T00:00:00 |
2019/10/15
|
https://itif.org/publications/2019/10/15/robotics-and-future-production-and-work/
|
[
{
"date": "2019/10/15",
"position": 73,
"query": "robotics job displacement"
},
{
"date": "2019/10/15",
"position": 90,
"query": "robotics job displacement"
}
] |
How Robots Are Beginning to Affect Workers and Their Wages
|
How Robots Are Beginning to Affect Workers and Their Wages
|
https://tcf.org
|
[
"William M. Rodgers Iii"
] |
... robotic growth is leading to widespread job displacement, as some have predicted. That said, there are winners and losers with automation ...
|
Executive Summary
Much has been written about the rise of robots and the potential impacts of automation on the economy. Yet most analysis tends to be prospective in nature, and estimates of future impacts on employment vary widely, with some studies predicting that as many as 50 percent of all workers are at risk of losing their jobs to automation. Even less is understood about the actual impacts of robots on jobs, wages, and workers today. While more recent studies have begun to measure these effects, the results here, too, are mixed.
This report analyzes the impact of robots in the years following the Great Recession, from 2009 to 2017—a period of significant, steady job growth and economic recovery, as well as one in which the use of robots in the U.S. workplace more than doubled. The report’s findings, summarized below, offer new insights that can help inform ongoing debates about the future of work and the impact of automation.
The first takeaway is that robots are, indeed, coming—but there is little evidence (yet) that robotic growth is leading to widespread job displacement, as some have predicted. That said, there are winners and losers with automation. While robots may have negligible effects on national employment as a whole, certain industries and regions are more impacted by robotic growth, and particular groups of workers disproportionately suffer the negative effects of this growth. It is also the case that job losses from robotization may have little impact on total employment, as displaced workers find other jobs (especially in a strong economy with low unemployment), even if at lower pay. Lastly, we find that the economic boom of the past decade has effectively “masked” some of the impacts that robots have had on workers. It’s not that robots weren’t displacing jobs—it’s that the overall economic expansion was large enough to offset some of these job losses.
Key Findings
1. Trends in Robot Growth
We constructed a measure of the use of robots—commonly referred to as “robot intensity”—to estimate trends in robot exposure across more than 250 metropolitan areas and over time, finding that:
During the Great Recession, robot intensity plummeted. But since 2009, robot intensity has sharply increased nationwide.
nationwide. States in the Midwest (the East North Central, or ENC, census division)— Michigan, Ohio, Indiana, Illinois, and Wisconsin —consistently have the highest robot intensities, typically at least twice the intensity of all other regions.
—consistently have the highest robot intensities, typically at least twice the intensity of all other regions. Midwest (ENC) states also experienced the sharpest growth in robots since 2009.
since 2009. Robot intensity in manufacturing industries greatly exceeds the national average: Since 2009, the number of manufacturing robots has more than doubled—from 0.813 per thousand workers to 1.974 per thousand workers.
greatly exceeds the national average: Highly unionized states have much lower robot intensities than states with low rates of unionization.
2. Impact of Robots Varies across Workers
We assess the impact of robot intensity on the employment and earnings outcomes of non-college educated men and women, finding that:
The adoption of robots since the Great Recession has been accompanied by employment gains for some groups of workers, and appears not to have affected other groups. Positive impact on: young, less-educated men and less-educated adult women. No impact on: young, less-educated women and less-educated adult men.
of workers, and appears The adoption of robots since the Great Recession has been accompanied by wage increases for some groups of workers, and appears not to have adversely affected other groups. Positive impact on: young, less-educated women and less-educated minority men and women. No significant impact on: less-educated adult men and women.
of workers, and appears Yet, robotization has adversely affected other types of workers at the national level—for example, in manufacturing industries.
Our findings suggest that, at the current stage and pace of robot growth, and with the right economic conditions in place, some workers without a college degree may benefit from robotization. This is perhaps due to robots stimulating demand for goods, creating new markets, and spurring wage growth.
3. Impacts of Robots in the Rust Belt
Given that the Midwest has the greatest concentration of robots and the fastest robot growth (and thus is most likely to show the effects of robots), we focus in on the ENC region and examine the effects of robots on employment and wages by race/ethnicity, gender, age, and industry, finding that:
In Midwest manufacturing industries, robots have sizably decreased employment for some groups of workers ( young, less-educated men and women ). Estimated impact: for an increase of one robot per thousand workers, the employment-to-population ratio falls by an estimated 3.5 percentage points.
for some groups of workers ( ). In Midwest manufacturing industries, robots have sizably decreased wages for some groups of workers ( young, less-educated men and women ). Estimated impact: an increase of one robot per thousand workers is associated with a 4.0 percent to 5.0 percent decline in wages.
for some groups of workers ( ). The biggest negative impact of robots was on young, less-educated black men and women—groups that already have lower wages and employment rates, on average.
Lastly, we isolate the impact of robotic growth to predict what would have happened in the region absent the economic boom that began in 2009, finding that:
What happened (with robots and recovery): employment-to-population ratio for young, less-educated workers increases from roughly 34 percent to around 45.5 percent.
(with robots and recovery): employment-to-population ratio for young, less-educated workers from roughly 34 percent to around What would have happened (with robots, no recovery): employment-to-population ratio for young, less-educated workers decreases from 34 percent to around 30 percent.
These findings demonstrate that in the Midwest: (1) the economic recovery of the last decade has effectively “masked” the impact of robots on employment—that, absent such a strong recovery, robots would have displaced many more jobs than they did; and (2) if manufacturing growth during the recovery had not relied so heavily on robots, there would be many more jobs available to workers, and minority young workers in particular.
Introduction
Although it took several years for the U.S. economy to rebound from the Great Recession, the economic expansion currently under way is by most accounts viewed as stellar. Since February 2010—eight months after the National Bureau of Economic Research’s Dating Committee declared the recession’s end—private sector job growth has occurred for 112 consecutive months, growing at a monthly average of 192,000, and creating over 21.3 million jobs through May 2019. The nation’s unemployment rate has fallen far below the 6 to 7 percent level that many economists and policymakers once believed was the “NAIRU”—the nonaccelerating inflation rate of unemployment, below which inflation supposedly would take off. (It hasn’t.)
As the current expansion unfolded, technological change continued to reshape how Americans work, where they work, when they work, and with whom they work. One example of this growing impact of technology in the workplace is the use of industrial robots in the workplace. According to the International Federation of Robotics (IFR), from 2009 to 2017, the use of robots in the U.S. workplace—often called “robot exposure”—more than doubled, from 0.75 robots per thousand workers to 1.81 robots per thousand workers. (See Figure 1.) Has this led to continued worker displacement effects, or are we seeing increases in employment due to robots’ productivity effect?
Figure 1
A previous study, by Daron Acemoglu of MIT and Pascual Restrepo of Boston University, looking at thirteen manufacturing industries and six nonmanufacturing industries from 1990 to 2007, found that an increased use of industrial robots—that is, what is referred to as increased “robot intensity,” typically measured as the increase in the number of robots per thousand workers—lowered employment rates and wages overall. Specifically, the study estimates that one more robot per thousand workers reduces the nation’s employment-to-population ratio by about 0.18 to 0.34 percentage points, and lowers wages by 0.25 to 0.50 percent.
Based on this and other research, we were interested in knowing how much the use of industrial robots cut into labor market improvements that occurred after the Great Recession. What would the employment picture look like today, for example, if the adoption and use of industrial robots had only occurred, without an economic boom to mask its impact? That is, how much did the recovery’s “tight” labor market offset robot’s displacement effects, or how much did robots add to employment growth? Further, were the impacts of increased robot intensity widespread, or were they targeted to particular demographic groups and regions of the country?
To explore these questions, we constructed a measure of robot intensity and used its variation across metropolitan areas (known in government data as metropolitan statistical areas, MSAs) ; and over time to identify whether robot exposure has negative impacts on the economic positions of young men and women, especially non-college-educated minority youth.
Our research presented us with several major takeaways. First, while labor market statistics such as the Bureau of Labor Statistics’ “official” unemployment rate currently look quite good, the positive trends of a persistently low jobless rate and strong job growth have not had the same beneficial effects on the employment and wages of youth and minorities as they did during the previous boom of the 1990s. (See Figure 2.)
Figure 2
Second, since the end of the Great Recession, we do find evidence of a small, national-level productivity effect for young less-educated men and less-educated adult women. That is, specifically, the adoption of robots increases their employment because increased robot usage (1) stimulates demand for the goods that robots produce, (2) create new markets, and (3) spurs wage growth.
However, our strongest evidence is for the existence of industrial-robot-led job displacement primarily in the nation’s East North Central (ENC) census division (Illinois, Indiana, Wisconsin, Michigan, and Ohio), with young, less-educated men and women bearing the brunt of the job losses. For an increase of one robot per thousand workers, the employment-to-population ratio of all young less-educated ENC workers falls by a precisely estimated 3.5 percentage points. This is nontrivial, given that the 2009 employment-to-population ratio for these groups is 35.0 percent. With respect to wages, an area’s one–robot-per-thousand-worker increase is associated with a 4.0 to 5.0 percent decline in the individual wages of young, less-educated men and women who live in the ENC census division and work in the manufacturing sector.
Third, even though job growth has not been as strong during this boom as it was during the 1990s, current economic growth has helped to offset the adverse impact that increased robot intensity has had on employment. For these same young, less-educated minority men and women in the ENC, if robot intensity was the only source of change from 2009 to 2017, their employment-population ratios would have fallen to approximately 30.0 percent instead of rising from 34.0 to 45.5 percent. Thus, our findings indicate that robotic growth has had its biggest negative impact on young, less-educated ENC black men and women. These men and women started at lower employment rates and they would have experienced the largest increases in employment if robotic growth was not as strong. Employment rates for young, less-educated black men and women in these heartland states are still well below the experience of other demographic groups, and the data indicates that they would have had more opportunity if not for use of industrial robots.
These findings reflect the insights of The Century Foundation’s High Wage America project —that there needs to be concerted efforts to expand access to good-paying job opportunities in manufacturing communities, especially where there are large African-American and Latinx populations. The findings support The Century Foundation’s framework and recommended actions that will help employers find skilled workers and provide opportunities for the region’s minority residents.
The Robots Are Coming
We are not the first to examine the impact of robots on labor market outcomes. The first generation of studies estimate the “risk” or chance that automation displaces workers. Several studies predict that, over the next two decades, 45 percent to 57 percent of U.S. and OECD workers will be at risk of losing their jobs to automation. More recent studies shift to estimating actual displacement effects. One widely cited study by Daron Acemoglu and Pascual Restrepo uses industry-country variation in robot usage from the International Federation of Robotics (IFR) data and finds that increasing a U.S. commuting area’s robot intensity reduces its labor market outcomes. Another widely cited study—by Georg Graetz at Uppsala University and Guy Michaels of the London School of Economics—also uses the IFR data but a different methodology, and shows that industrial robots increase productivity and wages, but reduce the number of jobs available to low-skilled workers. Specifically, the measure of industrial robot usage employed by Graetz and Michaels indicates that, for the overall economy, one more robot per thousand workers reduces the aggregate employment-to-population ratio by about 0.20 percentage points, which is equivalent to one new robot lowering employment by approximately 3.3 workers. With respect to wages, one more robot per thousand workers reduces wages by 0.37 percent.
Although not a major focus of their study, but relevant to our work, the estimated effects by industry, occupation, gender, and skill are reported. The study reports that the effects on the employment-to-population ratios of men and women are –0.53 and –0.30. The negative job effects for men tend to be in manufacturing, while women’s losses are larger in nonmanufacturing industries. Job loss is concentrated in manual occupations, such as machinists, assemblers, material handlers, and welders. Declines in employment and wages occur at all levels of educational attainment, but tend to be larger among less-educated workers.
These studies answer many questions, but raise others. What has happened since 2007? What are the racial and ethnic effects? How are young workers impacted by the adoption and spread of industrial robots? But before we proceed to answering these questions, it is important to understand the type of robots we are talking about.
First, what is an industrial robot? The IFR, whose data we use, defines an industrial robot as “an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes, which can be either fixed in place or mobile for use in industrial automation applications.”
The IFR identifies five types of industrial robots. Their mechanical structure determines the type. The types are linear (including Cartesian and gantry robots), Selective Compliance Assembly Robot Arm (SCARA), articulated robots, parallel robots (delta), and cylindrical. Articulated robots typically perform handling for metal casting, palletizing, welding, and painting. Linear robots are typically used for handling for plastic molding, sealing, laser-welding, pressing, packaging, or handling for forging. SCARA-type robots perform assembly and packaging tasks. Parallel robots carry out picking and placing tasks, assembly, and handling routines. Cylindrical robots perform spot welding.
The common thread for all of these robot types is that they are performing specific and repetitive tasks, with high volume. This is very different from artificial intelligence, which is the gathering of large quantities of data to make complex decisions. This distinction between robots and AI can help formulate predictions as to which types of workers will be disadvantaged. The general consensus is that robots are having a greater impact on less-educated workers, who can replace repetitive, often skilled tasks, of these workers.
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Where—and for Whom—Is Robot Intensity the Highest?
The robot intensity measure that we constructed to discover trends in metropolitan statistical areas from 2004 to 2017 indicated the following key results:
Prior to the Great Recession, robot intensity trended upward. During the recession, robot intensity plummeted. Since 2009, robot intensity has sharply increased.
States in East North Central (ENC) census division—Michigan, Ohio, Indiana, Illinois and Wisconsin—have the highest robot intensities.
Minorities and youth with no more than a high school diploma live in metropolitan areas that have similar industrial robot intensities as whites and adults.
Workers in Right-to-Work states experienced a gradual increase in their robot intensities.
Highly unionized states have the lowest robot intensities. However, they began to trend upward after 2015.
The top ten metropolitan statistical areas with respect to robot intensity are:
Los Angeles-Long Beach-Santa Ana, CA Chicago-Naperville-Joliet, IL Houston-Baytown-Sugar Land, TX Phoenix-Mesa-Scottsdale, AZ Detroit-Warren-Dearborn, MI Milwaukee-Waukesha-West Allis, WI Philadelphia-Camden-Wilmington, PA-NJ-DE-MD San Jose-Sunnyvale-Santa Clara, CA Indianapolis, IN Cleveland-Elyria, OH
We estimate MSA robot intensities from 2004 to 2017, and in a given year, link an area’s robot intensity to its respondents in the Current Population Survey Outgoing Rotation Group (CPS-ORG) micro-data files. To be included in our CPS-ORG samples, respondents must not be enrolled in school. Further, respondents are only included if their entries can be matched with valid information for the following variables: the metropolitan area’s unemployment rate; the metropolitan area’s percentage of employment that is in manufacturing; the respondent’s race, age, educational attainment, marital status, veteran status, state of residence; whether they are foreign born, and a U.S. citizen; whether they live in a central city, suburb, or rural area; and whether the respondent lives in a Right-to-Work state.
Our youth samples are for respondents 16 to 24 years of age. They are African American, Latinx, and white men and women who have completed no more than twelve years of schooling or received no more than a high school diploma or GED. Adult respondents are 25-to-64-year-old African American, Latinx, and white men and women. They, too, have completed not more than a high school degree.
Due to the uniqueness of the IFR robot stock and intensity measure that we construct, we first provide summary statistics on robot intensity. Table 1 reports annual averages of MSA-level estimates of robot intensity for respondents by race, ethnicity and gender. There are a few racial, ethnic, age and gender differences. For example, in 2017, adult Latinx men and women have intensities of 2.34 per thousand workers, well above the overall averages. (See Figure 3.) However, these differences will not be large enough to explain much if any of the racial and ethnic differences that exist in employment and wages.
Figure 3
Table 2 provides the annual averages from 2004 to 2017 for selected characteristics: private sector workers, respondents that live in Right-to-Work states, respondents with no more than a high school degree, respondents that live in “highly” unionized states, and respondents that live in the East North Central or Middle-Atlantic census divisions. (See Figure 4.) The East North Central is comprised of Illinois, Indiana, Michigan, Ohio, and Wisconsin. Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota and South Dakota make up the West North Central division. The “highly unionized” category corresponds to states with a unionization rate that exceeds 17.0 percent.
Figure 4
The most notable finding is that the respondents that live in East North Central MSAs consistently have robot intensities that are at least twice the intensity of all other regions. (See Figure 5.) These include Chicago, Detroit, Milwaukee, Cleveland, Indianapolis, Minneapolis, Columbus, Toledo, Fort Wayne, Rockford, Elkhart-Goshen, Canton, Akron, and South Bend, all in the top fifty metropolitan areas with the greatest robot intensities (Appendix Table 1).
Figure 5
Table 2 shows that respondents in manufacturing industries have intensities that exceed the national average, and since the recession’s end, the number of robots has jumped from 0.813 per thousand workers to 1.974 per thousand workers. At the other end of the spectrum, respondents that live in highly unionized states have much lower robot intensities, with a sizeable trending up since 2016. Surprisingly, respondents that live in Right-to-Work states have intensities that are below the national average; however, the intensity has more than doubled since 2009, the end of the recession. (See Figure 6.) Panel B of Table 2 reports robot intensity for Midwestern manufacturing. As a point of comparison, the table reports robot intensities for the West North Central division (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota and South Dakota), plus the Middle-Atlantic states (New York, Pennsylvania, and New Jersey, Delaware, Maryland, Washington, D.C., Virginia, and West Virginia). The table clearly shows that the variation is most pronounced in the East North Central states, as demonstrated as well by Figure 6. Manufacturing robot intensity almost doubled from 2009 to the present.
Figure 6
The Impacts of Robot Intensity on Employment and Wages
How much do the employment and earnings of non-college-educated men and women vary with an area’s robot intensity? To answer this question, we compared the economic outcomes of men and women across metropolitan areas with different exposure to robots, using the merged CPS-ORG files. We applied a linear probability model for our employment analysis. For our wage effects, we estimate a regression that captures the relationship between an area’s robot intensity and the inflation-adjusted wages of workers in that area. To account for other factors that impact employment and wages, both models include controls for whether the respondent lives in a Right-to-Work state; marital and veteran status; whether the respondent lives in an urban or suburban area; whether the respondent is foreign born and a U.S. citizen; the metropolitan statistical area’s percent of employment that is in manufacturing; and year and metropolitan area dummy variables.
Table 3 displays metro area fixed effect and instrumental variable estimates of the link between the employment of non-college-educated men and women to area robot intensity. The tables allow for a comparative analysis for all men and women (males and females 25 to 64 years old). The estimated coefficients indicate how an increase in one robot per thousand workers impacts a particular group’s employment-to-population ratio and wages.
The estimated coefficients on the African-American and Latinx dummy variables indicate that controlling for racial and ethnic differences in robot intensity still leaves a large employment gap between African Americans and whites. The black–white employment gaps for less-educated young men and for less-educated adult men are 14.9 and 11.9 percent, respectively. Among women, the racial employment gap ranges from 4.7 to 11.4 percent. Unlike factors such as education, training, and discrimination, racial and ethnic differences in robot intensity or exposure explain none of the employment gaps between minorities and whites.
While robots can’t explain the black–white employment gap, two different statistical methods (the fixed-effect and instrumental variable estimates) both show statistically significant impacts of the increasing use of robots on national productivity and displacement of certain workers. The estimates for all less-educated young men, less-educated African American and Latinx young men, and less-educated adult women suggest that metro area increases in robot intensity are associated with a slight increase in employment (productivity effect). An increase in one robot per thousand workers leads to a 1.2 to 2.0 percentage point increase in their employment-to-population ratios. We can only speculate, but it seems reasonable to think that robot adoption stimulates demand for the goods that robots produce, creates new markets, and spurs wage growth, all creating an increased demand for these workers.
The actual change in robot intensities for these groups from 2004 to 2017 expands by just over one robot per thousand people. Over this same period, the employment-to-population ratio of young, less-educated men falls from 70.3 percent to 66.1 percent. Thus, the productivity effect worked to offset the actual decline in the employment-to-population ratio of young, less-educated men. Young, less-educated women and less-educated adult men are not impacted by an area’s robot intensity. This could be attributed to these groups not working directly in those industries where robot adoption is occurring.
With respect to wages, we only see an impact on the wages of young, less-educated women, and less-educated minority men and women. Higher robot intensity increases wages for these groups. Less educated adult men and women are not adversely impacted by increased robot intensity. These results are quite different than what the study by Acemoglu and Restrepo finds. That study’s national level results for the general population suggest an adverse impact on employment and wages: an additional robot per thousand workers reduces the employment-to-population ratio by about 0.18 to 0.34 percentage points and wages by 0.25 to 0.50 percent.
Why do our estimates differ from those of Acemoglu and Restrepo? There are several potential explanations. We create robot intensity measures for 262 MSAs, while they construct their robot intensity measures for 722 commuting zones. Our samples also differ. We focus on youth and adults who have no more than a high school degree, while they focus on the overall population. A priori, we thought that the employment and wages of these workers would be quite sensitive to an area’s robot exposure. The productivity effects that we find for young, less-educated men and less-educated adult women suggest that even workers with the least education may benefit from the increased use of robots. Maybe some workers with no more than a high school degree are in jobs that are not as easily taken over by robots, or they benefit from a multiplier effect. New higher productivity/high wage jobs generate demand for jobs that workers with no more than a high school degree fill. We may need to focus on respondents with slightly higher levels of educational attainment (for example, high school and AA degrees).
We do agree with Acemoglu and Restrepo that the robot intensity effects are concentrated in Rust Belt manufacturing sectors of the East North Central (ENC) census division. Appendix Table 1 shows that most metropolitan areas have intensity measures that are basically zero, while the ENC has not only the highest intensities, but also had the sharpest growth from 2004 to 2017. Thus, displacement effects, if they exist in the data, are more likely to have a regional and industry component to them. Focusing on sectors and regions that are directly impacted by robots may be a better identification strategy.
To implement this identification strategy, we limit the samples to men and women that live in the East North Central census division. Table 3 also reports the employment and wage effects for the ENC division by race/ethnicity and age. These are based on respondents that live in Ohio, Illinois, Indiana, Michigan, and Wisconsin. It is important to note that to maintain sample sizes that yield reliable results, we pool men and women. We also pool African-American and Latinx respondents. Entries in the column labeled “ENC Men and Women” suggest that the use of industrial robots does not have displacement or productivity effects on ENC employment. Maybe they offset each other. The estimated coefficients for young ENC less-educated men and women and adults are basically zero. A negative effect of -0.010 may exist for young, less-educated black and Latinx men, but the standard errors indicate the estimate has little precision. The wage results show no evidence that an area’s growth in robot intensity is associated with a decline in the area’s wages.
The last column of Panel A of Table 3 reports estimates for only manufacturing workers in the ENC census division. These estimates indicate that robot intensity has a displacement effect. For an increase of one robot per thousand workers, the employment-to-population ratio of all young, less-educated men and women falls by a precisely estimated 3.5 percentage points. (See Figure 7.) This is nontrivial given that the 2009 employment-population ratio for these groups is 35.0 percent. All less-educated ENC adult men and women experience neither a displacement nor a productivity effect. Panel B of Table 3 shows that an area increase of one robot per thousand workers is associated with a 4.0 to 5.0 percent decline in the wages of young, less-educated men and women who live in the ENC census division and work in the manufacturing sector. The wages of less-educated adults are not adversely impacted.
Figure 7
There appear to be limited spillover impacts on the general population of less-educated workers, but although not measured with precision, there are negative impacts on the employment of manufacturing workers, especially if they work in manufacturing and reside in the East North Central census division.
To conclude this section, we generated the predicted ENC employment rates by race and age based on the actual change in robot intensities from 2009 to 2017. For example, we created an estimate of the youth employment-to-population ratio assuming that robot intensity was the only source of change from 2009 to 2017. We then compared it to what actually happened to the youth employment-to-population ratio. Table 4 presents the predictions. For less-educated ENC youth, their employment-to-population ratio actually increases from approximately 34.0 percent to around 45.5 percent. If robot intensity was the only source of change between 2009 and 2017, the employment-to-population ratio of ENC youth would have fallen to approximately 30.0 percent. (See Figure 8.) Robotic growth has its biggest negative impact on young, less-educated ENC black men and women. These men and women started at lower employment rates and they would have experienced the largest increases in employment if robotic growth was not as strong. While the employment prospects of less-educated ENC youth were still quite low relative to prime-age workers in 2017, the economic expansion was strong enough such that they experienced a net improvement in employment prospects during that time period.
Figure 8
The implication of these findings is that, if manufacturing growth during the expansion had not relied so heavily on robots, there would be more jobs in particular for minorities, as described in Table 4 and Figure 9, which shows that employment rates of young, less-educated minority workers in the East North Central division have been depressed by automation. Robots have a particular impact on repetitive production jobs, such as machine operators, that have provided a source of good pay for young adult minorities in the industrial heartland. Our model indicates that this impact was large enough to decrease employment among these workers who have limited alternative opportunities, as evident by their already low employment-population ratios. Fortunately, the overall economic expansion was large enough to offset the job losses associated with robot adoption. Still, the employment levels of young, less-educated workers in the Rust Belt are far lower than other groups and robots appear to be having an impact.
Figure 9
It is worth noting that the use of robots contributes to the productivity and competitiveness of the economy and manufacturing sector, and the sector’s ability to increase output even as factory employment has not increased as much as it had in past cycles. For those workers that remain, industrial robots may actually be saving jobs—as robots make it possible for firms to competitively produce in the United States and compete with low-wage nations such as Mexico. This effect is not captured in our model and is an important caveat to the results.
Conclusions
With respect to the employment displacement effects of industrial robots, it is unclear as to whether adoption will accelerate in the East North Central division’s manufacturing sector and spread to other census divisions and industries; however, past experience and current research suggests the transformation that has been going on for decades will continue. During periods of previous adoption and diffusion of technology, numerous prominent policymakers, leaders, and economists predicted that the new technology was going to create widespread and crushing job displacement. Yet, there is little evidence to support this claim.
However, if the impact of robots is similar to the introduction of previous technologies, as shown in this report, there are “winners” and “losers.” Depending on the size of the individual and societal impacts, policymakers and employers may need to proactively work to ensure that they can find the skilled workers needed to operate and maintain these robots. Communities may need to provide education and training opportunities for new jobs and occupations that are created as a result of the robot technology. More importantly, policymakers, unions, employers, and nonprofit social service organizations will need to assist workers, especially young minority and women workers that get displaced. This is important because, even though there is no recession on the horizon, economic growth has begun to slow, meaning the ability of a “rising tide” to offset the negative aspects of robots will diminish.
Finally, the experiences of young Midwestern minority and women workers, employers, and their communities can help other parts of the country prepare for and minimize the economic, social, and cultural adjustment costs associated with the introduction and diffusion of robots. For example, The Century Foundation’s recent work on reinvigorating Chicago’s manufacturing sector and expanding economic opportunities of the city’s African-American and Latinx residents provides an excellent framework and outlines a series of actions that help employers find skilled workers and provide opportunity for the region’s minority residence. First, they propose the creation of public–private partnerships that support technological innovation in new products and increased efficiency of existing firms. Second, they propose the development of proactive initiatives that have the goal of retaining, reshoring, and revitalizing sustainable manufacturing jobs. Third, they promote capital strategies that rejuvenate manufacturing communities. Finally, they propose stimulating demand for manufacturing products via public procurement and infrastructure projects.
Table 1
Summary Statistics on by Age, Race, Gender, and Ethnicity of Metropolitan Area Robot Intensity, 2004 to 2017
(Robots per Thousand Workers) U.S. African American Latinx Women Men Women Men Women Men Year Youth Adult Youth Adult Youth Adult Youth Adult Youth Adult Youth Adult 2004 0.451 0.448 0.453 0.453 0.484 0.530 0.529 0.509 0.507 0.495 0.508 0.533 2005 0.527 0.523 0.537 0.526 0.575 0.619 0.588 0.588 0.582 0.565 0.575 0.595 2006 0.599 0.583 0.594 0.586 0.672 0.668 0.659 0.646 0.653 0.637 0.655 0.660 2007 0.666 0.650 0.674 0.658 0.773 0.747 0.744 0.737 0.716 0.733 0.743 0.749 2008 0.725 0.710 0.728 0.711 0.829 0.814 0.797 0.798 0.814 0.808 0.852 0.816 2009 0.759 0.747 0.764 0.757 0.825 0.822 0.850 0.817 0.851 0.845 0.841 0.862 2010 0.838 0.811 0.836 0.822 1.002 0.917 0.930 0.897 0.908 0.896 0.882 0.931 2011 0.899 0.883 0.896 0.896 1.089 0.992 1.065 0.959 0.989 0.977 0.966 1.013 2012 0.965 0.966 0.960 0.960 1.091 1.084 1.059 1.049 1.087 1.065 1.021 1.100 2013 1.077 1.067 1.075 1.075 1.240 1.198 1.190 1.197 1.302 1.181 1.174 1.199 2014 1.267 1.244 1.299 1.299 1.345 .3441 1.354 1.349 1.524 1.443 1.508 1.482 2015 1.564 1.530 1.557 1.557 1.567 1.517 1.509 1.511 1.988 1.936 1.946 2.005 2016 1.803 1.804 1.838 1.838 1.537 1.731 1.749 1.673 2.346 2.331 2.372 2.333 2017 1.810 1.792 1.824 1.824 1.619 1.686 1.667 1.675 2.364 2.313 2.375 2.339 Notes: Notes: The entries are annual averages of MSA-level estimates of robot intensity for respondents with a particular characteristic. Similar to the metropolitan area unemployment rate, the area Robot intensity measure is linked to an individual’s micro data. We construct the intensity estimate as follows. MSA-level robot intensity for MSA i in year t is adapted from the commuter-based measure in Acemoglu and Restrepo (2017). MSA i’s exposure in year t is written as follows: MSA’s exposure to robots in year t=Σ l si 2000 (RU S i,t ⁄ LU Si,t ) i∈I Where l si 2000 corresponds to the 2000 share of MSA s employment in industry i, which we construct from the 2000 MORG files of the CPS. The term RU S i,t is the ith industry’s robot intensity in year t at the U.S. level. This data comes from the IFR stock data base. The LU Si,t denotes at the national level, the ith industry’s total employment in year t.
Table 2
Summary Statistics on U.S. Metropolitan Area Robot Intensity, 2004 to 2017 (Robots per Thousand Workers) Panel A: Selected Characteristics Year All Industries Private Sector Right-to-Work Less Educated and Not Enrolled ENC WNC Middle-Atlantic Highly Unionized Manufacturing Only 2004 0.451 0.461 0.328 0.451 1.188 0.369 0.325 0.407 0.495 2005 0.526 0.534 0.388 0.514 1.368 0.415 0.385 0.457 0.570 2006 0.587 0.595 0.437 0.577 1.515 0.460 0.427 0.503 0.633 2007 0.657 0.670 0.491 0.652 1.702 0.517 0.478 0.559 0.728 2008 0.713 0.727 0.534 0.711 1.852 0.565 0.520 0.549 0.777 2009 0.754 0.766 0.565 0.741 1.970 0.596 0.544 1.792 0.813 2010 0.820 0.835 0.625 0.810 2.125 0.651 0.593 0.523 0.893 2011 0.891 0.909 0.693 0.871 2.295 0.697 0.647 0.730 0.989 2012 0.969 0.991 0.763 0.939 2.507 0.770 0.706 0.502 1.066 2013 1.073 1.100 0.931 1.047 2.756 0.867 0.777 0.583 1.186 2014 1.253 1.280 1.031 1.235 3.032 0.955 0.838 0.376 1.420 2015 1.545 1.573 1.083 1.534 3.290 1.076 0.866 0.357 1.733 2016 1.811 1.836 1.213 1.789 3.579 1.194 0.948 0.717 1.984 2017 1.805 1.825 1.779 3.583 1.208 0.941 1.805 1.974 Notes: The entries are annual averages of MSA-level estimates of robot intensity for respondents with a particular characteristic. The categories correspond to the following: private sector workers, whether the respondent lives in a Right-to-Work state, whether they have no more than a high school degree, whether the live in one of the three census divisions, whether the respondent lives in a highly unionized state. The ENC is comprised of Michigan, Ohio, Indiana, Wisconsin, and Minnesota. The WNC states are Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota and South Dakota. Highly unionized corresponds to states with unionization rate that exceeds 17.0 percent which is the unionization rate at the 75th percentile of the state-level union membership distribution for the period from 2004 to 2016. Similar to the metropolitan area unemployment rate, the area Robot intensity measure is linked to an individual’s micro data. The robot intensity for MSA i in year t is adapted from the commuter-based measure in Acemoglu and Restrepo (2018). MSA i’s exposure in year t is written as follows: MSA’s exposure to robots in year t=Σ l si 2000 (RU S i,t ⁄ LU S i,t ) i∈I Where l si 2000 corresponds to the 2000 share of MSA s employment in industry i, which we construct from the 2000 MORG files of the CPS. The term RU S i,t is the ith industry’s robot intensity in year t at the U.S. level. This data comes from the IFR stock data base. The LU Si,t denotes at the national level, the ith industry’s total employment in year t.
Table 2 cont.
Summary Statistics on Regional Manufacturing Metropolitan Area Robot Intensity, 2004 to 2017 (Robots per Thousand Workers) Panel B: Manufacturing Only Manufacturing Only Year U.S. Manufacturing Only ENC WNC Middle-Atlantic 2004 0.495 1.050 0.385 0.302 2005 0.570 1.166 0.425 0.373 2006 0.633 1.276 0.472 0.397 2007 0.728 1.454 0.525 0.439 2008 0.777 1.559 0.564 0.484 2009 0.813 1.730 0.601 0.501 2010 0.893 1.934 0.665 0.543 2011 0.989 2.061 0.729 0.577 2012 1.066 2.190 0.795 0.639 2013 1.186 2.410 0.898 0.705 2014 1.420 2.658 0.966 0.797 2015 1.733 2.786 1.113 0.818 2016 1.984 3.055 1.290 0.849 2017 1.974 3.169 1.209 0.883 Notes: The entries are annual averages of MSA-level estimates of robot intensity for respondents with a particular characteristic. Similar to the metropolitan area unemployment rate, the area Robot intensity measure is linked to an individual’s micro data. We construct the intensity estimate as follows. The robot intensity for MSA i in year t is adapted from the commuter-based measure in Acemoglu and Restrepo (2017). MSA i ’s exposure in year t is written as follows: MSA’s exposure to robots in year t=Σ l si 2000 (RU S i,t ⁄ LU Si,t ) i∈I Where l si 2000 corresponds to the 2000 share of MSA s employment in industry i, which we construct from the 2000 MORG files of the CPS. The term RU S i,t is the ith industry’s robot intensity in year t at the U.S. level. This data comes from the IFR stock data base. The LU Si,t denotes at the national level, the ith industry’s total employment in year t.
Table 3
Instrumental Variable and Fixed Effect Employment and Wage Models Panel A: Employment All Manufacturing Only Men Women ENC Men and Women Men Women ENC Men and Women Young Less Educated Robot intensity 0.012 a 0.003 -0.002 -0.013 -0.074 -0.035 a (0.003) (0.003) (0.004) (0.013) (0.048) (0.013) Area unemployment rate -0.014 a -0.007 a -0.013 a -0.030 a -0.007 -0.006 (0.002) (0.002) (0.004) (0.008) (0.015) (0.018) African American -0.149 a -0.114 a -0.151 a -0.071 a -0.145 a -0.109 c (0.007) (0.007) (0.010) (0.027) (0.050) (0.066) Latinx -0.005 -0.045 a -0.007 0.005 -0.011 0.054 (0.007) (0.005) (0.011) (0.019) (0.037) (0.056) Young, Less-Educated Black and Latinx Robot intensity 0.020 a 0.002 -0.010 -0.009 -0.051 -0.037 (0.004) (0.004) (0.007) (0.024) (0.072) (0.025) Area unemployment rate -0.017 a -0.005 c -0.002 -0.031 a 0.007 0.014 (0.003) (0.003) (0.007) (0.012) (0.025) (0.044) African American -0.136 a -0.064 a -0.135 a -0.103 a -0.176 a -0.133 (0.010) (0.009) (0.012) (0.036) (0.057) (0.090) All Less Educated Adult Robot intensity 0.001 0.010 a 0.001 -0.002 -0.010 a 0.001 (0.002) (0.002) (0.002) (0.004) (0.004) (0.005) Area unemployment rate -0.013 a -0.002 -0.007 b -0.018 a -0.010 a -0.032 a (0.001) (0.001) (0.004) (0.002) (0.004) (0.005) African American -0.119 a -0.042 a -0.093 a -0.069 a -0.038 a -0.077 a (0.006) (0.010) (0.008) (0.008) (0.011) (0.009) Latinx 0.012 b 0.007 0.039 a -0.006 0.003 0.000 (0.006) (0.008) (0.012) (0.006) (0.011) (0.007) Adult, Less-Educated Black and Latinx Robot intensity 0.003 0.014 a 0.001 0.002 -0.016 a 0.006 (0.003) (0.004) (0.004) (0.004) (0.005) (0.010) Area unemployment rate -0.011 a -0.006 a -0.003 -0.014 a -0.013 c -0.027 a (0.002) (0.002) (0.006) (0.004) (0.008) (0.008) African American -0.111 a -0.047 a -0.107 a -0.045 a -0.040 a -0.055 a (0.009) (0.017) (0.010) (0.013) (0.014) (0.017) Notes: Calculated from the U.S. Bureau of the Census Current Population Survey’s Annual Merged Outgoing Rotation Group files, 2004 to 2017. The entries are coefficients from linear probability models which include year and MSA dummy variables, dummy variables for race and ethnicity, whether the respondent lives in a Right-to-Work state, their age, marital and veteran status and educational attainment, whether the live in an urban, suburban or rural area, whether the respondent is foreign born and a U.S. citizen, and the Metropolitan area’s percent of employment that is in manufacturing. Robust standard errors are in parentheses. a 1 percent level of significance. b 5 percent level of significance. c 10 percent level of significance.
Table 3 cont.
Instrumental Variable and Fixed Effect Employment and Wage Models Panel B: Wages All Manufacturing Only Men Women ENC Men and Women Men Women ENC Men and Women Young, Less-Educated Robot intensity 0.006 0.020a -0.001 0.001 0.018 -0.041a (0.005) (0.005) (0.005) (0.036) (0.035) (0.013) Area unemployment rate -0.008a -0.009a -0.002 -0.011 -0.018 0.016 (0.003) (0.003) (0.008) (0.008) (0.012) (0.019) African American -0.084a -0.009c -0.053a -0.057a -0.109a -0.093a (0.005) (0.005) (0.010) (0.021) (0.040) (0.033) Latinx -0.023a 0.006 -0.002 -0.039a 0.019 -0.015 (0.006) (0.006) (0.006) (0.019) (0.026) (0.032) Young, Less-Educated Black and Latinx Robot intensity 0.017a 0.025a 0.010 0.001 0.038 -0.050b (0.007) (0.006) (0.008) (0.040) (0.040) (0.020) Area unemployment rate -0.005 -0.009a 0.007 -0.004 -0.007 0.051c (0.004) (0.003) (0.009) (0.012) (0.017) (0.029) African American -0.057a -0.021b -0.067a -0.012 -0.119c -0.112a (0.008) (0.009) (0.014) (0.029) (0.062) (0.052) All Less-Educated Adult Robot intensity -0.005 -0.006 -0.009a 0.007 -0.005 -0.002 (0.007) (0.006) (0.002) (0.007) (0.012) (0.005) Area unemployment rate -0.002 0.000 0.002 0.001 0.003 0.002 (0.002) (0.002) (0.004) (0.003) (0.005) (0.007) African American -0.211a -0.068a -0.148a -0.182a -0.106a -0.160a (0.006) (0.005) (0.019) (0.013) (0.017) (0.028) Latinx -0.097a -0.050a -0.050a -0.104a -0.105a -0.064a (0.007) (0.006) (0.014) (0.011) (0.014) (0.018) Adult, Less-Educated Black and Latinx Robot intensity 0.004 0.006 -0.006 0.015b -0.015 -0.011 (0.013) (0.011) (0.006) (0.007) (0.012) (0.011) Area unemployment rate -0.007b -0.005b -0.002 -0.005 0.007 -0.003 (0.003) (0.003) (0.006) (0.005) (0.006) (0.013) African American -0.113a -0.016c -0.110a -0.093a 0.000 -0.122a (0.007) (0.009) (0.018) (0.016) (0.027) (0.031) Notes: Calculated from the U.S. Bureau of the Census Current Population Survey’s Annual Merged Outgoing Rotation Group files, 2004 to 2016. The entries are coefficients from linear probability models which include year and MSA dummy variables, dummy variables for race and ethnicity, whether the respondent lives in a Right-to-Work state, their age, marital and veteran status and educational attainment, whether the live in an urban, suburban or rural area, whether the respondent is foreign born and a U.S. citizen, and the Metropolitan area’s percent of employment that is in manufacturing. Robust standard errors are in parentheses. a 1 percent level of significance. b 5 percent level of significance. c 10 percent level of significance.
Table 4
| 2019-10-17T00:00:00 |
2019/10/17
|
https://tcf.org/content/report/robots-beginning-affect-workers-wages/
|
[
{
"date": "2019/10/17",
"position": 36,
"query": "robotics job displacement"
},
{
"date": "2019/10/17",
"position": 35,
"query": "robotics job displacement"
},
{
"date": "2019/10/17",
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"query": "robotics job displacement"
},
{
"date": "2019/10/17",
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"query": "robotics job displacement"
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"date": "2019/10/17",
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"query": "robotics job displacement"
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{
"date": "2019/10/17",
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"query": "robotics job displacement"
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{
"date": "2019/10/17",
"position": 40,
"query": "robotics job displacement"
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{
"date": "2019/10/17",
"position": 37,
"query": "robotics job displacement"
},
{
"date": "2019/10/17",
"position": 38,
"query": "robotics job displacement"
},
{
"date": "2019/10/17",
"position": 34,
"query": "robotics job displacement"
},
{
"date": "2019/10/17",
"position": 50,
"query": "robotics job displacement"
}
] |
Risks and remedies for artificial intelligence in health care | Brookings
|
Risks and remedies for artificial intelligence in health care
|
https://www.brookings.edu
|
[
"Alex Engler",
"Cameron F. Kerry",
"Isabelle Hau",
"Rebecca Winthrop",
"Elaine Kamarck",
"John W. Mcarthur",
"Zia Khan",
"Jacob Taylor",
"Clea Mcelwain"
] |
Several risks and challenges emerge, including the risk of injuries to patients from AI system errors, the risk to patient privacy of data acquisition and AI ...
|
This report from The Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative is part of “ AI Governance ,” a series that identifies key governance and norm issues related to AI and proposes policy remedies to address the complex challenges associated with emerging technologies.
Introduction
Injuries and error. The most obvious risk is that AI systems will sometimes be wrong, and that patient injury or other health-care problems may result. If an AI system recommends the wrong drug for a patient, fails to notice a tumor on a radiological scan, or allocates a hospital bed to one patient over another because it predicted wrongly which patient would benefit more, the patient could be injured. Of course, many injuries occur due to medical error in the health-care system today, even without the involvement of AI. AI errors are potentially different for at least two reasons. First, patients and providers may react differently to injuries resulting from software than from human error. Second, if AI systems become widespread, an underlying problem in one AI system might result in injuries to thousands of patients—rather than the limited number of patients injured by any single provider’s error.
Managing patients and medical resources. Finally, and least visibly to the public, AI can be used to allocate resources and shape business. For instance, AI systems might predict which departments are likely to need additional short-term staffing, suggest which of two patients might benefit most from scarce medical resources, or, more controversially, identify revenue-maximizing practices.
Automating drudgery in medical practice. AI can automate some of the computer tasks that take up much of medical practice today. Providers spend a tremendous amount of time dealing with electronic medical records, reading screens, and typing on keyboards, even in the exam room. 4 If AI systems can queue up the most relevant information in patient records and then distill recordings of appointments and conversations down into structured data, they could save substantial time for providers and might increase the amount of facetime between providers and patients and the quality of the medical encounter for both.
Democratizing medical knowledge and excellence. AI can also share the expertise and performance of specialists to supplement providers who might otherwise lack that expertise. Ophthalmology and radiology are popular targets, especially because AI image-analysis techniques have long been a focus of development. Several programs use images of the human eye to give diagnoses that otherwise would require an ophthalmologist. Using these programs, general practitioner, technician, or even a patient can reach that conclusion. 3 Such democratization matters because specialists, especially highly skilled experts, are relatively rare compared to need in many areas.
Although the field is quite young, AI has the potential to play at least four major roles in the health-care system: 1 Pushing boundaries of human performance. The flashiest use of medical AI is to do things that human providers—even excellent ones—cannot yet do. For instance, Google Health has developed a program that can predict the onset of acute kidney injury up to two days before the injury occurs; compare that to current medical practice, where the injury often isn’t noticed until after it happens. 2 Such algorithms can improve care beyond the current boundaries of human performance.
Artificial intelligence (AI) is rapidly entering health care and serving major roles, from automating drudgery and routine tasks in medical practice to managing patients and medical resources. As developers create AI systems to take on these tasks, several risks and challenges emerge, including the risk of injuries to patients from AI system errors, the risk to patient privacy of data acquisition and AI inference, and more. Potential solutions are complex but involve investment in infrastructure for high-quality, representative data; collaborative oversight by both the Food and Drug Administration and other health-care actors; and changes to medical education that will prepare providers for shifting roles in an evolving system.
Data availability. Training AI systems requires large amounts of data from sources such as electronic health records, pharmacy records, insurance claims records, or consumer-generated information like fitness trackers or purchasing history. But health data are often problematic. Data are typically fragmented across many different systems. Even aside from the variety just mentioned, patients typically see different providers and switch insurance companies, leading to data split in multiple systems and multiple formats. This fragmentation increases the risk of error, decreases the comprehensiveness of datasets, and increases the expense of gathering data—which also limits the types of entities that can develop effective health-care AI.
Privacy concerns. Another set of risks arise around privacy.5 The requirement of large datasets creates incentives for developers to collect such data from many patients. Some patients may be concerned that this collection may violate their privacy, and lawsuits have been filed based on data-sharing between large health systems and AI developers.6 AI could implicate privacy in another way: AI can predict private information about patients even though the algorithm never received that information. (Indeed, this is often the goal of health-care AI.) For instance, an AI system might be able to identify that a person has Parkinson’s disease based on the trembling of a computer mouse, even if the person had never revealed that information to anyone else (or did not know). Patients might consider this a violation of their privacy, especially if the AI system’s inference were available to third parties, such as banks or life insurance companies.
Bias and inequality. There are risks involving bias and inequality in health-care AI. AI systems learn from the data on which they are trained, and they can incorporate biases from those data. For instance, if the data available for AI are principally gathered in academic medical centers, the resulting AI systems will know less about—and therefore will treat less effectively—patients from populations that do not typically frequent academic medical centers. Similarly, if speech-recognition AI systems are used to transcribe encounter notes, such AI may perform worse when the provider is of a race or gender underrepresented in training data.7
“Even if AI systems learn from accurate, representative data, there can still be problems if that information reflects underlying biases and inequalities in the health system.”
Even if AI systems learn from accurate, representative data, there can still be problems if that information reflects underlying biases and inequalities in the health system. For example, African-American patients receive, on average, less treatment for pain than white patients;8 an AI system learning from health-system records might learn to suggest lower doses of painkillers to African-American patients even though that decision reflects systemic bias, not biological reality. Resource-allocation AI systems could also exacerbate inequality by assigning fewer resources to patients considered less desirable or less profitable by health systems for a variety of problematic reasons.
Professional realignment. Longer-term risks involve shifts in the medical profession. Some medical specialties, such as radiology, are likely to shift substantially as much of their work becomes automatable. Some scholars are concerned that the widespread use of AI will result in decreased human knowledge and capacity over time, such that providers lose the ability to catch and correct AI errors and further to develop medical knowledge.9The nirvana fallacy. One final risk bears mention. AI has the potential for tremendous good in health care. The nirvana fallacy posits that problems arise when policymakers and others compare a new option to perfection, rather than the status quo. Health-care AI faces risks and challenges. But the current system is also rife with problems. Doing nothing because AI is imperfect creates the risk of perpetuating a problematic status quo.
Possible solutions
There are several ways we can deal with possible risks of health-care AI:
Data generation and availability. Several risks arise from the difficulty of assembling high-quality data in a manner consistent with protecting patient privacy. One set of potential solutions turns on government provision of infrastructural resources for data, ranging from setting standards for electronic health records to directly providing technical support for high-quality data-gathering efforts in health systems that otherwise lack those resources. A parallel option is direct investment in the creation of high-quality datasets. Reflecting this direction, both the United States’ All of Us initiative and the U.K.’s BioBank aim to collect comprehensive health-care data on huge numbers of individuals. Ensuring effective privacy safeguards for these large-scale datasets will likely be essential to ensuring patient trust and participation.
Quality oversight. Oversight of AI-system quality will help address the risk of patient injury. The Food and Drug Administration (FDA) oversees some health-care AI products that are commercially marketed. The agency has already cleared several products for market entry, and it is thinking creatively about how best to oversee AI systems in health. However, many AI systems in health care will not fall under FDA’s purview, either because they do not perform medical functions (in the case of back-end business or resource-allocation AI) or because they are developed and deployed in-house at health systems themselves—a category of products FDA typically does not oversee. These health-care AI systems fall into something of an oversight gap. Increased oversight efforts by health systems and hospitals, professional organizations like the American College of Radiology and the American Medical Association, or insurers may be necessary to ensure quality of systems that fall outside the FDA’s exercise of regulatory authority.10
“A hopeful vision is that providers will be enabled to provide more-personalized and better care. … A less hopeful vision would see providers struggling to weather a monsoon of uninterpretable predictions and recommendations from competing algorithms.”
Provider engagement and education. The integration of AI into the health system will undoubtedly change the role of health-care providers. A hopeful vision is that providers will be enabled to provide more-personalized and better care, freed to spend more time interacting with patients as humans.11 A less hopeful vision would see providers struggling to weather a monsoon of uninterpretable predictions and recommendations from competing algorithms. In either case—or in any option in-between—medical education will need to prepare providers to evaluate and interpret the AI systems they will encounter in the evolving health-care environment.
The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.
Microsoft provides support to The Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative, and Google provides general, unrestricted support to the Institution. The findings, interpretations, and conclusions in this report are not influenced by any donation. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.
| 2019-11-14T00:00:00 |
https://www.brookings.edu/articles/risks-and-remedies-for-artificial-intelligence-in-health-care/
|
[
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{
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{
"date": "2019/11/14",
"position": 26,
"query": "artificial intelligence healthcare"
},
{
"date": "2019/11/14",
"position": 25,
"query": "artificial intelligence healthcare"
},
{
"date": "2019/11/14",
"position": 27,
"query": "artificial intelligence healthcare"
},
{
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"query": "artificial intelligence healthcare"
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|
5 Top Careers in Artificial Intelligence
|
5 Top Careers in Artificial Intelligence
|
https://graduate.northeastern.edu
|
[
"Meghan Gocke"
] |
Job Outlook: Data analysts have a positive career outlook. These roles earn an average salary of $61,307 per year. Establishing a Career in AI.
|
Artificial intelligence (AI) has come to define society today in ways we never anticipated. AI makes it possible for us to unlock our smartphones with our faces, ask our virtual assistants questions and receive vocalized answers, and have our unwanted emails filtered to a spam folder without ever having to address them.
These kinds of functions have become so commonplace in our daily lives that it’s often easy to forget that, just a decade ago, few of them existed. Yet while artificial intelligence and machine learning may have been the topic of conversation among science fiction enthusiasts since the ’80s, it wasn’t until much more recently that computer scientists acquired the advanced technology and the extensive amount of data needed to create the products we use today.
The impact of machine learning and AI doesn’t stop at the ability to make the lives of individuals easier, however. These programs have been developed to positively impact almost every industry through the streamlining of business processes, the improving of consumer experiences, and the carrying out of tasks that have never before been possible.
This impact of AI across industries is only expected to increase as technology continues to advance and computer scientists uncover the exciting possibilities of this specialization in their field. Below, we explore what exactly artificial intelligence entails, what careers are currently defining the industry, and how you can set yourself up for success in the AI sector.
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What is Artificial Intelligence?
The term “artificial intelligence” has many connotations, depending on the specific industry it is used in. Most often, however, “when people say ‘artificial intelligence,’ what they actually mean is machine learning,” says Bethany Edmunds, associate dean and lead faculty at Northeastern’s Khoury College of Computer Science. “[Although AI] is a large umbrella term that incorporates a lot of statistical methods, historically, what it actually means is a computer acting like a human.”
The ability of a computer to replicate human-like behavior is at the core of all AI functions. Machine learning software allows computers to “witness” human behavior through the intake of data. These systems then undergo advanced processes to analyze that data and identify patterns within it, using those findings to apply the discovered knowledge and replicate the behavior.
Edmunds identifies that, while advanced technology is important in this process, the key to the operation is actually the data. In fact, the astounding increase in the quantity of data collected over the last decade has had a significant impact on the advancement of the AI industry today.
“What’s happening right now is that the technology has finally caught up to what people have been predicting [about AI] for a long time,” she says. “We finally have the right amount of data and the advanced machines that can process that data, which is why, right now, [AI] is being applied in so many sectors.”
Despite the exciting opportunities that these advances are bringing to light, some individuals are still quite skeptical about the use of AI. Edmunds believes that this is due, in large part, to a lack of understanding about exactly how these processes work and the fear that comes with that.
“I like to equate [the introduction of AI] to cloud computing; while people don’t necessarily know how Google Drive works, they understand the concept and are faster to participate in…putting their information in cloud storage,” she says. “AI is not like that. People don’t understand the statistics behind it…so it all just seems very magical.”
Those who have a complex understanding of computer science and statistics, however, recognize that the potential impact of this function is endless. “AI is doing amazing things today and allowing for developments across industries that we’ve never seen before,” Edmunds says.
5 Top Careers in Artificial Intelligence
As the possible applications of AI continue to increase, so does the positive career potential for those with the skills needed to thrive in this industry. The World Economic Forum’s “The Future of Jobs 2018“ report predicts that there will be 58 million new jobs in artificial intelligence by 2022.
However, those with the necessary combination of skills are often hard to come by, Edmunds explains. “The job market is really huge in [AI], but a lot of people aren’t trained for it,” she says, resulting in an above-average job outlook for those who do have the skills needed to work in this niche area.
Read on to explore some of these top career areas defining the industry.
1. Artificial Intelligence Research
Although many of these top careers explore the application or function of AI technology, computer science and artificial intelligence research is more about discovering ways to advance the technology itself. “There will always be somebody developing a faster machine,” Edmunds says. “There’s always going to be somebody pushing the edge, and that [person] will be a computer scientist.”
Responsibilities: A computer science and artificial intelligence researcher’s responsibilities will vary greatly depending on their specialization or their particular role in the research field. Some may be in charge of advancing the data systems related to AI. Others might oversee the development of new software that can uncover new potential in the field. Others still may be responsible for overseeing the ethics and accountability that comes with the creation of such tools. No matter their specialization, however, individuals in these roles will work to uncover the possibilities of these technologies and then help implement changes in existing tools to reach that potential.
Career Outlook: As these individuals are at the crux of advancement in AI, their job outlook is very positive. The New York Times estimates that high-level AI researchers at top companies make more than $1,000,000 per year as of 2018, with lower-level employees making between $300,000 and $500,000 per year in both salary and stock. Individuals in base-level AI research roles are likely to make an average salary of $92,221 annually.
2. Software Engineering
The AI field also relies on traditional computer science roles such as software engineers to develop the programs on which artificial intelligence tools function.
Responsibilities: Software engineers are part of the overall design and development process of digital programs or systems. In the scope of AI, individuals in these roles are responsible for developing the technical functionality of the products which utilize machine learning to carry out a variety of tasks.
Career Outlook: The Bureau of Labor Statistics predicts a growth rate of 22 percent by 2029 for software developers, including the addition of 316,000 jobs. Software engineers also make an average salary of $110,140 per year, with potential increases for those with a specialty in AI.
3. Natural Language Processing
Many of the most popular consumer applications of AI today revolve around language. From chatbots to virtual assistants to predictive texting on smartphones, AI tools have been used to replicate human speech in a variety of formats. To do this effectively, developers call upon the knowledge of natural language processers—individuals who have both the language and technology skills needed to assist in the creation of these tools. “Natural language processing is applying machine learning to language,” Edmunds says. “It’s a really big field.”
Responsibilities: As there are many applications of natural language processing, the responsibilities of the experts in this field will vary. However, in general, individuals in these roles will use their complex understanding of both language and technology to develop systems through which computers can successfully communicate with humans.
Career Outlook: “There’s a real shortage of people in these roles [today],” Edmunds says. “There are a bunch of [products] where we’re trying to interact with a machine through language, but language is really hard.” For this reason, those with the proper skill sets can expect an above-average salary and job outlook for the foreseeable future. The average annual salary for those with natural language processing skills is $107,641 per year.
4. User Experience
User experience (UX) roles involve working with products—including those which incorporate AI—to ensure that consumers understand their function and can easily use them. Although Edmunds emphasizes that these roles do exist outside of the artificial intelligence sector, the increased use of AI in technology today has led to a growing need for UX specialists that are trained in this particular area.
Responsibilities: In general, user experience specialists are in charge of understanding how humans use equipment, and thus how computer scientists can apply that understanding to the production of more advanced software. In terms of AI, a UX specialist’s responsibilities may include understanding how humans are interacting with these tools in order to develop functionality that better fits those humans’ needs down the line.
Did You Know: One of the most prominent examples of how user experience influenced technology we know today is Apple. The invention of Mac operating software—compared to Windows—came from the need for a product that was more user-friendly and which didn’t require an advanced technical understanding to operate. Apple approached the development of the iPhone in the same way. “The iPhone was all about user experience,” Edmunds says. “That was a [user experience expert] understanding how people interact [with their phones], including what’s intuitive and what’s not. Then they designed the best possible phone to fit those needs.”
Job Outlook: The job outlook for user experience designers is quite positive. The average salary for UX designers is $76,440 per year (though those at the top of their field make over $100,000 annually). Job growth in this industry is expected to increase by 22.1 percent by 2022, effectively increasing opportunities for those with the right training and experience.
5. Data Analytics
With data at the heart of AI and machine learning functions, those who have been trained to properly manage that data have many opportunities for success in the industry. Though data science is a broad field, Edmunds emphasizes the role that data analysts play in these AI processes as one of the most significant.
Responsibilities: Data analysts need to have a solid understanding of the data itself—including the practices of managing, analyzing, and storing it—as well as the skills needed to effectively communicate findings through visualization. “It’s one thing to just have the data, but to be able to actually report on it to other people is vital,” Edmunds says.
Job Outlook: Data analysts have a positive career outlook. These roles earn an average salary of $61,307 per year.
Establishing a Career in AI
Artificial intelligence is a lucrative field with above-average job growth, but the industry remains competitive. Roles in this discipline are very niche, requiring both an advanced technical background and extensive hands-on experience. Those with this rare balance of skills and real-world exposure will be able to land any number of roles in AI and continue shaping the landscape of this constantly evolving field for years to come.
Hone Your Skills
Artificial intelligence professionals share an array of practical skills and theoretical knowledge in mathematics and statistics, alongside a working understanding of role-specific tools and processes. Some AI-focused computer scientists may also pursue an understanding of the ethics and philosophy that go into giving a computer the capability to “think” and draw conclusions.
However, Edmunds emphasizes that, while quite advanced, these common abilities alone do not always set an individual up for a successful career in artificial intelligence. Instead, she explains, it’s the personal backgrounds and unique interdisciplinary skills each computer scientist brings to the table that allow them to thrive.
“One of the most important factors of AI is an understanding of the application,” she says. “Somebody needs to look at the data [these tools use] and understand what that actually means for their specific sector.”
In healthcare, for instance, an ideal AI specialist would have an understanding of data and machine learning, as well as a working knowledge of the human body. In this scenario, the specialist’s background in both areas allows them not only to interpret the conclusions of these AI tools, but also understand how they fit into the broader context of health.
Edmunds has also observed that, while a computer scientist with a dual background is ideal for the new kinds of applications of AI across industries, very few currently exist. “If you had a dual background, you would be able to write your own check,” Edmunds jokes. “I can assure you, you wouldn’t be looking for a job right now.”
Instead of this ideal candidate, those in AI often see machine learning experts with high-level computer science and statistics abilities but without a further grasp in any particular domain. This, Edmunds identifies, is the missing piece needed for further sector-specific AI advancement.
To bridge this gap, artificial intelligence programs like those at Northeastern look to embrace students’ personal backgrounds or prior career paths and develop artificial intelligence specialists with the ability to make a real difference across industries.
Earn an Advanced Degree
Those looking to either break into or advance their careers in artificial intelligence can benefit from obtaining a master’s degree at a top university like Northeastern.
Those hoping to work in AI should instead consider a Master of Science in Artificial Intelligence to hone their skills, learn from top industry leaders, and obtain the real-world experience they need to properly develop a specialized career.
These practices allow Northeastern’s students to prepare for their future in the changing field of artificial intelligence while always keeping the real-world aspect of their work in mind. “Through experiential learning and interdisciplinary integration, [Northeastern’s] master’s programs are focused on developing the professional,” Edmunds says. “All the course work is centered around real-world problems or application domains, and we do our best to get industry practitioners in the classroom to make sure what we’re doing is cutting edge.”
While Northeastern emphasizes the benefits of experiential learning across all of its graduate and undergraduate programs, these opportunities allow AI students specifically to practice what they’re learning in the classroom at some of the top companies in the world.
Did You Know: Northeastern has developed an array of regional campuses in locations across North America that are known for their top tech talent, including Seattle, the San Francisco Bay Area, Toronto, Charlotte, and Vancouver. These regional locations have allowed unique partnerships to develop between the university and local organizations, which happen to be among the top companies in the world. Popular co-op locations for students in these areas include Amazon, Facebook, Microsoft, Nordstrom, and Google, alongside many other leading organizations.
Northeastern’s artificial intelligence program provides the rare opportunity to learn from top industry leaders, work with some of the most famous companies in the world, and develop not only relevant AI and computer science skills but those which align with your preferred specialization all before you graduate. Consider enrolling to take the first step toward a fulfilling career in the exciting artificial intelligence field.
| 2019-11-18T00:00:00 |
2019/11/18
|
https://graduate.northeastern.edu/knowledge-hub/career-in-artificial-intelligence/
|
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What jobs are affected by AI? Better-paid, better-educated workers ...
|
What jobs are affected by AI? Better-paid, better-educated workers face the most exposure
|
https://www.brookings.edu
|
[
"Mark Maccarthy",
"Eduardo Levy Yeyati",
"Xiang Hui",
"Oren Reshef"
] |
AI could affect work in virtually every occupational group. However, whereas research on automation's robotics and software continues to show ...
|
AI consists of a diverse set of technologies that serve a variety of purposes. Therefore, no single definition can yet capture its full set of operations and capabilities. However, broadly speaking, AI involves programming computers to do things which—if done by humans—would be said to require “intelligence,” whether it be planning, learning, reasoning, problem-solving, perception, or prediction.
Contrary to other forms of automation, such as robotics and software, researchers have had little time to learn about AI’s primary use cases in the economy.
To circumvent many of the problems posed by AI for labor market analysis then, this brief leverages a novel method created by Stanford Ph.D. candidate Michael Webb to quantify the exposure of occupations to AI, in order to assess the broader labor market impacts. (See Michael Webb, “The Impact of Artificial Intelligence on the Labor Market.”)
…
Along these lines, the present analysis uses machine learning in the form of natural language processing to quantify the overlap between text from patents filed for AI technologies, and job descriptions from the U.S. Department of Labor’s O*NET database.
This process allowed Webb to generate a measure of every occupation’s varying levels of “exposure to AI applications in the near future.” These scores were then normalized to aid in comparing them with one another. As a result, “exposure” scores in this paper do not indicate the percentage of tasks that can be replaced by AI, but rather indicate each job’s relative exposure above (positive numbers) or below (negative numbers) the average job’s exposure to AI.
Read more about what AI is and how we’re measuring it on page 5 of the full report. »
| 2019-11-20T00:00:00 |
https://www.brookings.edu/articles/what-jobs-are-affected-by-ai-better-paid-better-educated-workers-face-the-most-exposure/
|
[
{
"date": "2019/11/20",
"position": 48,
"query": "AI impact jobs"
}
] |
|
Why these companies are rethinking the use of AI in hiring | PBS News
|
Why these companies are rethinking the use of AI in hiring
|
https://www.pbs.org
|
[
"Zoe Rohrich"
] |
A growing body of research indicates that artificial intelligence systems used for job recruitment, which have become increasingly common, reinforce racial and ...
|
A growing body of research indicates that artificial intelligence systems used for job recruitment, which have become increasingly common, reinforce racial and gender inequality. Now, innovators are hoping to spur a kind of course correction by developing software that promises more accountability, and combats — rather than perpetuates — employment discrimination.
When AI was initially introduced into the hiring process, the potential benefits seemed promising. Not only would algorithms expedite information processing, the developers said, they had the capability to counter bias often found in human decision-making. The idea was that a computer would not hold the same biases as a person and not judge a candidate based on gender or race.
But now that AI has been used for several years, it is clear that software often reinforces inequality in job recruitment, instead of reducing it.
Computer algorithms are not biased because of a technical glitch or because of some sci-fi scenario in which the robot takes on a personality of its own. Many AI hiring systems are created using a company’s previous applicant history — which historically, has been favorable to cisgender, white men.
In short, AI systems are biased because people are, and the realization has forced some companies to rethink their use of AI in hiring.
“If you are building a model using what has been historically successful, it automatically skews the rating system to favor what has been historically representative, which we know to be male, and predominantly white,” said Stephanie Lampkin, CEO and Founder of Blendoor, a job recruiting platform that hides candidates’ names and photos when matching them with companies, so that individuals can be evaluated based solely on skill or experience.
In short, AI systems are biased because people are, and the realization has forced some companies to rethink their use of AI in hiring.
Last year, Amazon got rid of its AI job recruiting tool after discovering that it was biased against women. Amazon had trained its algorithms to rate resumes based on patterns in past applicant history — but because women were so rare in that data set, the algorithm believed men were preferable, and poorly rated women’s applications.
Some entrepreneurs and developers think there are still effective ways to use AI in recruiting — with some adjustments.
Lampkin started Blendoor four years ago after she found herself repeatedly hitting walls in the hiring process. She said the difficult part of addressing bias is getting access to larger and more inclusive data sets.
At any given point, Blendoor is using algorithms to match about 105,000 candidates to open jobs at various companies. Job seekers can upload their resumes to the site, but the company also makes a specific effort to recruit people who are otherwise underrepresented in the workforce. That includes people of color, women and people with disabilities, by partnering with colleges — including historically black colleges and universities — and professional organizations.
Lampkin said in one instance, a company that used Blendoor to hire interns increased their underrepresented minority by six times the amount they had recruited the previous year.
Even the most advanced AI won’t affect the results of job recruitment if the data isn’t using the full scope of the qualified talent pool, Lampkin explained, adding that companies seem to care about these results, too.
Another way tech gurus are looking to reduce bias in AI hiring is by increasing accountability.
Data scientist Cathy O’Neil, the founder of O’Neil Risk Consulting & Algorithmic Auditing, or ORCAA, is developing the concept of the “accountable algorithm.” The idea is that algorithms should be checked or “audited” for fairness.
“Algorithms aren’t evil or inherently good. They can have negative effects in a given context,” O’Neil said. “Right now we’re asked to put — with blind faith — trust in whatever algorithms give to us. I’m trying to go to the next step and ask, what does it mean to have a trustworthy algorithm?”
For O’Neil, a trustworthy algorithm incorporates a third-party verification model. ORCAA works with companies to test their algorithms and determine whether they are disadvantaging any specific group. ORCAA then helps adjust the algorithms and retests them to make sure the issues are resolved. Once the concerns are addressed, the companies are awarded an ORCAA Seal of Approval, meaning their algorithms have been tested for issues like accuracy, bias, consistency, transparency, fairness and legal compliance.
It’s a third-party seal of approval that O’Neil said she hopes will become more widespread in the future.
“That is one model that I really enjoy thinking about,” said O’Neil. “In the future, stakeholders should be like, wait, why did this work? Why should we trust you?”
Dr. Safiya Umoja Noble, who co-directs the Center for Critical Internet Inquiry at UCLA and has studied how bias appears in search engines like Google, argues that companies themselves have to be held legally accountable. She said it’s insufficient to blame the issue on a product’s algorithm — especially when it’s a product that’s highly profitable or impactful in society.
“We need civil rights and human rights protections around the output of these systems, and what they actually do,” Noble said. “That’s the thing to look at, because there is no ‘nirvana state’ where there is going to be an unbiased algorithm.”
| 2019-11-26T00:00:00 |
2019/11/26
|
https://www.pbs.org/newshour/world/agents-for-change/why-these-companies-are-rethinking-the-use-of-ai-in-hiring
|
[
{
"date": "2019/11/26",
"position": 96,
"query": "AI hiring"
},
{
"date": "2019/11/26",
"position": 97,
"query": "AI hiring"
},
{
"date": "2019/11/26",
"position": 84,
"query": "AI hiring"
},
{
"date": "2019/11/26",
"position": 72,
"query": "AI hiring"
}
] |
Bringing Artificial Intelligence into Pay Decisions - SHRM
|
Bringing Artificial Intelligence into Pay Decisions
|
https://www.shrm.org
|
[
"Joanne Sammer"
] |
AI could help develop compensation metrics that reward employee efforts to advance an organization's goals.
|
Designed and delivered by HR experts to empower you with the knowledge and tools you need to drive lasting change in the workplace.
Demonstrate targeted competence and enhance credibility among peers and employers.
Gain a deeper understanding and develop critical skills.
| 2019-12-09T00:00:00 |
https://www.shrm.org/topics-tools/news/benefits-compensation/bringing-artificial-intelligence-pay-decisions
|
[
{
"date": "2019/12/09",
"position": 56,
"query": "AI wages"
},
{
"date": "2019/12/09",
"position": 66,
"query": "AI wages"
},
{
"date": "2019/12/09",
"position": 54,
"query": "AI wages"
},
{
"date": "2019/12/09",
"position": 71,
"query": "artificial intelligence wages"
},
{
"date": "2019/12/09",
"position": 59,
"query": "AI wages"
},
{
"date": "2019/12/09",
"position": 56,
"query": "AI wages"
},
{
"date": "2019/12/09",
"position": 92,
"query": "artificial intelligence wages"
},
{
"date": "2019/12/09",
"position": 97,
"query": "artificial intelligence wages"
}
] |
|
Artificial intelligence will help determine if you get your next job - Vox
|
Artificial intelligence will help determine if you get your next job
|
https://www.vox.com
|
[
"Rebecca Heilweil"
] |
Recruiters can make use of artificial intelligence throughout the hiring process, from advertising and attracting potential applicants to ...
|
With parents using artificial intelligence to scan prospective babysitters’ social media and an endless slew of articles explaining how your résumé can “beat the bots,” you might be wondering whether a robot will be offering you your next job.
We’re not there yet, but recruiters are increasingly using AI to make the first round of cuts and to determine whether a job posting is even advertised to you. Often trained on data collected about previous or similar applicants, these tools can cut down on the effort recruiters need to expend in order to make a hire. Last year, 67 percent of hiring managers and recruiters surveyed by LinkedIn said AI was saving them time.
But critics argue that such systems can introduce bias, lack accountability and transparency, and aren’t guaranteed to be accurate. Take, for instance, the Utah-based company HireVue, which sells a job interview video platform that can use artificial intelligence to assess candidates and, it claims, predict their likelihood to succeed in a position. The company says it uses on-staff psychologists to help develop customized assessment algorithms that reflect the ideal traits for a particular role a client (usually a company) hopes to hire for, like a sales representative or computer engineer.
Output of an Artificial Intelligence system from Google Vision, performing facial recognition on a photograph of a man in San Ramon, California on November 22, 2019. Smith Collection/Gado/Getty Images
That algorithm is then used to analyze how individual candidates answer preselected questions in a recorded video interview, grading their verbal responses and, in some cases, facial movements. HireVue claims the tool — which is used by about 100 clients, including Hilton and Unilever — is more predictive of job performance than human interviewers conducting the same structured interviews.
But last month, lawyers at the Electronic Privacy Information Center (EPIC), a privacy rights nonprofit, filed a complaint with the Federal Trade Commission, pushing the agency to investigate the company for potential bias, inaccuracy, and lack of transparency. It also accused HireVue of engaging in “deceptive trade practices” because the company claims it doesn’t use facial recognition. (EPIC argues HireVue’s facial analysis qualifies as facial recognition.)
The lawsuit follows the introduction of the Algorithmic Accountability Act in Congress earlier this year, which would grant the FTC authority to create regulations to check so-called “automated decision systems” for bias. Meanwhile, the Equal Opportunity Employment Commission (EEOC) — the federal agency that deals with employment discrimination — is reportedly now investigating at least two discrimination cases involving job decision algorithms, according to Bloomberg Law.
AI can pop up throughout the recruitment and hiring process
Recruiters can make use of artificial intelligence throughout the hiring process, from advertising and attracting potential applicants to predicting candidates’ job performance. “Just like with the rest of the world’s digital advertisement, AI is helping target who sees what job descriptions [and] who sees what job marketing,” explains Aaron Rieke, a managing director at Upturn, a DC-based nonprofit digital technology research group.
And it’s not just a few outlier companies, like HireVue, that use predictive AI. Vox’s own HR staff use LinkedIn Recruiter, a popular tool that uses artificial intelligence to rank candidates. Similarly, the jobs platform ZipRecruiter uses AI to match candidates with nearby jobs that are potentially good fits, based on the traits the applicants have shared with the platform — like their listed skills, experience, and location — and previous interactions between similar candidates and prospective employers. For instance, because I applied for a few San Francisco-based tutoring gigs on ZipRecruiter last year, I’ve continued to receive emails from the platform advertising similar jobs in the area.
Join the Open Sourced Reporting Network Christina Animashaun/Vox Open Sourced is Recode by Vox’s year-long reporting project to demystify the world of data, personal privacy, algorithms, and artificial intelligence. And we need your help. Fill out this form to contribute to our reporting.
Overall, the company says its AI has trained on more than 1.5 billion employer-candidate interactions.
Platforms like Arya — which says it’s been used by Home Depot and Dyson — go even further, using machine learning to find candidates based on data that might be available on a company’s internal database, public job boards, social platforms like Facebook and LinkedIn, and other profiles available on the open web, like those on professional membership sites.
Arya claims it’s even able to predict whether an employee is likely to leave their old job and take a new one, based on the data it collects about a candidate, such as their promotions, movement between previous roles and industries, and the predicted fit of a new position, as well as data about the role and industry more broadly.
Another use of AI is to screen through application materials, like résumés and assessments, in order to recommend which candidates recruiters should contact first. Somen Mondal, the CEO and co-founder of one such screening and matching service, Ideal, says these systems do more than automatically search résumés for relevant keywords.
For instance, Ideal can learn to understand and compare experiences across candidates’ résumés and then rank the applicants by how closely they match an opening. “It’s almost like a recruiter Googling a company [listed on an application] and learning about it,” explains Mondal, who says his platform is used to screen 5 million candidates a month.
But AI doesn’t just operate behind the scenes. If you’ve ever applied for a job and then been engaged by a text conversation, there’s a chance you’re talking to a recruitment bot. Chatbots that use natural-language understanding created by companies like Mya can help automate the process of reaching out to previous applicants about a new opening at a company, or finding out whether an applicant meets a position’s basic requirements — like availability — thus eliminating the need for human phone-screening interviews. Mya, for instance, can reach out over text and email, as well as through messaging applications like Facebook and WhatsApp.
Another burgeoning use of artificial intelligence in job selection is talent and personality assessments. One company championing this application is Pymetrics, which sells neuroscience computer games for candidates to play (one such game involves hitting the spacebar whenever a red circle, but not a green circle, flashes on the screen).
These games are meant to predict candidates’ “cognitive and personality traits.” Pymetrics says on its website that the system studies “millions of data points” collected from the games to match applicants to jobs judged to be a good fit, based on Pymetrics’ predictive algorithms.
Proponents say AI systems are faster and can consider information human recruiters can’t calculate quickly
These tools help HR departments move more quickly through large pools of applicants and ultimately make it cheaper to hire. Proponents say they can be more fair and more thorough than overworked human recruiters skimming through hundreds of résumés and cover letters.
“Companies just can’t get through the applications. And if they do, they’re spending — on average — three seconds,” Mondal says. “There’s a whole problem with efficiency.” He argues that using an AI system can ensure that every résumé, at the very least, is screened. After all, one job posting might attract thousands of applications, with a huge share from people who are completely unqualified for a role.
Such tools can automatically recognize traits in the application materials from previous successful hires and look for signs of that trait among materials submitted by new applicants. Mondal says systems like Ideal can consider between 16 and 25 factors (or elements) in each application, pointing out that, unlike humans, it can calculate something like commute distance in “milliseconds.”
“You can start to fine-tune the system with not just the people you’ve brought in to interview, or not just the people that you’ve hired, but who ended up doing well in the position. So it’s a complete loop,” Mondal explains. “As a human, it’s very difficult to look at all that data across the lifecycle of an applicant. And [with AI] this is being done in seconds.”
These systems typically operate on a scale greater than a human recruiter. For instance, HireVue claims the artificial intelligence used in its video platform evaluates “tens of thousands of factors.” Even if companies are using the same AI-based hiring tool, they’re likely using a system that’s optimized to their own hiring preferences. Plus, an algorithm is likely changing if it’s continuously being trained on new data.
Another service, Humantic, claims it can get a sense of candidates’ psychology based on their résumés, LinkedIn profiles, and other text-based data an applicant might volunteer to submit, by mining through and studying their use of language (the product is inspired by the field of psycholinguistics). The idea is to eliminate the need for additional personality assessments. “We try to recycle the information that’s already there,” explains Amarpreet Kalkat, the company’s co-founder. He says the service is used by more than 100 companies.
Proponents of these recruiting tools also claim that artificial intelligence can be used to avoid human biases, like an unconscious preference for graduates of a particular university, or a bias against women or a racial minority. (But AI often amplifies bias; more on that later.) They argue that AI can help strip out — or abstract — information related to a candidate’s identity, like their name, age, gender, or school, and more fairly consider applicants.
The idea that AI might clamp down on — or at least do better than — biased humans inspired California lawmakers earlier this year to introduce a bill urging fellow policymakers to explore the use of new technology, including “artificial intelligence and algorithm-based technologies,” to “reduce bias and discrimination in hiring.”
These AI systems are only as good as the data they’re trained on and the humans that build them. If a résumé-screening machine learning tool is trained on historical data, such as résumés collected from a company’s previously hired candidates, the system will inherit both the conscious and unconscious preferences of the hiring managers who made those selections. That approach could help find stellar, highly qualified candidates. But Rieke warns that method can also pick up “silly patterns that are nonetheless real and prominent in a data set.”
One such résumé-screening tool identified being named Jared and having played lacrosse in high school as the best predictors of job performance, as Quartz reported.
If you’re a former high school lacrosse player named Jared, that particular tool might not sound so bad. But systems can also learn to be racist, sexist, ageist, and biased in other nefarious ways. For instance, Reuters reported last year that Amazon had created a recruitment algorithm that unintentionally tended to favor male applicants over female applicants for certain positions. The system was trained on a decade of résumés submitted to the company, which Reuters reported were mostly from men.
A visitor at Intel’s Artificial Intelligence (AI) Day walks past a signboard in Bangalore, India on April 4, 2017. Manjunath Kiran/AFP via Getty Images
(An Amazon spokesperson told Recode that the system was never used and was abandoned for several reasons, including because the algorithms were primitive and that the models randomly returned unqualified candidates.)
Mondal says there is no way to use these systems without regular, extensive auditing. That’s because, even if you explicitly instruct a machine learning tool not to discriminate against women, it might inadvertently learn to discriminate against other proxies associated with being female, like having graduated from a women’s college.
“You have to have a way to make sure that you aren’t picking people who are grouped in a specific way and that you’re only hiring those types of people,” he says. Ensuring that these systems are not introducing unjust bias means frequently checking that new hires don’t disproportionately represent one demographic group.
But there’s skepticism that efforts to “de-bias” algorithms and AI are a complete solution. And Upturn’s report on equity and hiring algorithms notes that “[de-biasing] best practices have yet to crystallize [and] [m]any techniques maintain a narrow focus on individual protected characteristics like gender or race, and rarely address intersectional concerns, where multiple protected traits produce compounding disparate effects.”
And if a job is advertised on an online platform like Facebook, it’s possible you won’t even see a posting because of biases produced by that platform’s algorithms. There’s also concern that systems like HireVue’s could inherently be built to discriminate against people with certain disabilities.
Critics are also skeptical of whether these tools do what they say, especially when they make broad claims about a candidates’ “predicted” psychology, emotion, and suitability for a position. Adina Sterling, an organizational behavior professor at Stanford, also notes that, if not designed carefully, an algorithm could drive its preferences toward a single type of candidate. Such a system might miss a more unconventional applicant who could nevertheless excel, like an actor applying for a job in sales.
“Algorithms are good for economies of scale. They’re not good for nuance,” she explains, adding that she doesn’t believe companies are being vigilant enough when studying the recruitment AI tools they use and checking what these systems actually optimize for.
Employment lawyer Mark Girouard says AI and algorithmic selection systems fall under the Uniform Guidelines on Employee Selection Procedures, guidance established in 1978 by federal agencies that guide companies’ selection standards and employment assessments.
Many of these AI tools say they follow the four-fifths rule, a statistical “rule of thumb” benchmark established under those employee selection guidelines. The rule is used to compare the selection rate of applicant demographic groups and investigate whether selection criteria might have had an adverse impact on a protected minority group.
But experts have noted that the rule is just one test, and Rieke emphasizes that passing the test doesn’t imply these AI tools do what they claim. A system that picked candidates randomly could pass the test, he says. Girouard explains that as long as a tool does not have a disparate impact on race or gender, there’s no law on the federal level that requires that such AI tools work as intended.
In its case against HireVue, EPIC argues that the company has failed to meet established AI transparency guidelines, including artificial intelligence principles outlined by the Organization for Economic Co-operation and Development that have been endorsed by the U.S and 41 other countries. HireVue told Recode that it follows the standards set by the Uniform Guidelines, as well as guidelines set by other professional organizations. The company also says its systems are trained on a diverse data set and that its tools have helped its clients increase the diversity of their staff.
At the state level, Illinois has made some initial headway in promoting the transparent use of these tools. In January, its Artificial Intelligence Video Interview Act will take effect, which requires that employers using artificial intelligence-based video analysis technology notify, explain, and get the consent of applicants.
Still, Rieke says few companies release the methodologies used in their bias audits in “meaningful detail.” He’s not aware of any company that has released the results of an audit conducted by a third party.
Meanwhile, senators have pushed the EEOC to investigate whether biased facial analysis algorithms could violate anti-discrimination laws, and experts have previously warned the agency about the risk of algorithmic bias. But the EEOC has yet to release any specific guidance regarding algorithmic decision-making or artificial intelligence-based tools and did not respond to Recode’s request for comment.
Rieke did highlight one potential upside for applicants. Should lawmakers one day force companies to release the results of their AI hiring selection systems, job candidates could gain new insight into how to improve their applications. But as to whether AI will ever make the final call, Sterling says that’s a long way’s off.
“Hiring is an extremely social process,” she explains. “Companies don’t want to relinquish it to tech.”
| 2019-12-12T00:00:00 |
2019/12/12
|
https://www.vox.com/recode/2019/12/12/20993665/artificial-intelligence-ai-job-screen
|
[
{
"date": "2019/12/12",
"position": 90,
"query": "AI hiring"
},
{
"date": "2019/12/12",
"position": 93,
"query": "AI hiring"
},
{
"date": "2019/12/12",
"position": 97,
"query": "AI hiring"
},
{
"date": "2019/12/12",
"position": 82,
"query": "artificial intelligence hiring"
}
] |
The Research Challenges of the AI Labor Market Challenges
|
The Research Challenges of the AI Labor Market Challenges
|
https://medium.com
|
[
"Golub Capital Social Impact Lab",
"Stanford Gsb"
] |
The World Economic Forum estimates that AI technology could displace 75 million jobs worldwide, while creating 133 million new roles to complement the new ...
|
Advances in technology will determine the jobs firms seek to fill and the skills workers need to fill them. Technological change has been, and likely will continue to be, skill-biased. As firms increasingly leverage technology to complete routine tasks, the complexity of tasks for workers operating the technology generally increases (Spitz-Oener, 2006). Thus, technology has the potential to displace jobs with tasks easily performed by machines and change the skill requirements for jobs complementary to or augmented by technology. Workers around the world have begun to feel their skills falling behind. For example, a 2016–2017 Organisation for Economic Co-operation and Development survey of workers found that 33.5% of respondents in the United States and 35.7% of all OECD respondents perceived a mismatch between their skills and the skills required by their jobs (OECD, 2016).
As societies face an increasingly turbulent labor market, it is natural to consider whether the necessary adjustments will occur organically through market mechanisms or whether government policies can help to ease the burden of these long-run changes on individuals. At this time, there are many crucial but unanswered questions. We seek to understand the cost-benefit decisions firms and individuals are making and the roles for firms, governments, and workers to prepare for technology-driven labor market changes.
Firms are solving a cost-benefit problem.
Firms may benefit from actively training their existing workers rather than hiring new workers. First, training reduces the time and resources a firm devotes to recruiting new employees. These search costs can be especially high in an already tight labor market and when high-demand skills are in low supply in the labor pool (Mühlemann & Leiser, 2018). Second, a firm’s existing employees already possess firm-specific skills and institutional knowledge that would be costly to develop in a new employee. Third, firms can retain existing workers’ social capital when they choose to keep and reskill their workers rather than fire them (Cappelli, 2004)¹. Fourth, there is some evidence that announcements of layoffs can harm a firm by decreasing remaining employees’ morale and productivity, or lowering public opinion and, therefore, stock prices (De Meuse et al., 1994).
Finally, firms have an information advantage when sourcing from their existing pool of employees. To the extent that firms observe and evaluate existing employees, a company likely already knows about an employee’s motivation, personality, social skills, and leadership qualities — components of a successful employee often unobservable on a CV or in an interview. Hence, the informational advantage of sourcing from a firm’s existing workers versus outside hires could justify retraining at least a subset of those workers.
Weighing against the benefits, a large-scale firm-sponsored retraining initiative has the potential to be extremely expensive. Absent a guarantee that the investment will pay off, firms may reasonably be skeptical of the value of such an initiative. Workers at risk of displacement tend to be workers whose skills and on-the-job experience might not translate easily into the skills needed for future work. These workers may struggle to transition into new roles, have a shorter work life than new workers, and be hard to retrain. Moreover, human resource departments don’t seem to have good systems in place to evaluate existing skills in the company, nor to predict skills demanded in the near future. And to the extent that firms select only their top employees to participate in retraining programs, the workers not selected for retraining will be the workers who struggle most to find new jobs.
Currently, firms seem generally reluctant to invest in retraining and upskilling their existing workers. This trend is especially true in the United States. As a recent example, an Accenture survey of 1,200 CEOs and executives working with AI shows that only 3% of respondents planned to significantly increase training budgets over the next three years, while more than half of the respondents cited growing skill gaps as a significant business concern (Accenture, 2018). Externally hiring new talent still seems to be the default strategy for many US firms.
Ultimately, we need continued research to better understand who can be retrained, which skills are easiest to train in existing workers, and which “old skills” best complement “new jobs.” Researchers are beginning to study skill similarities across different jobs and occupation transitions (World Economic Forum, 2018b, Alabdulkareem et al., 2018), but actionable recommendations for employers, policymakers, and educators alike are still sparse.
A role for government intervention?
To the extent that firms may not internalize all of the benefits of investing in worker retraining, they may lack the incentives to sponsor training programs of a scale needed to temper technology-driven economic disruption.² However, the public incentives to help at-risk workers transition to new jobs are clear. Job displacement and layoffs impose externalities on the broader economy, including increased reliance on social benefits, spillovers to local economies, and substantial long-term unemployment. Meanwhile, at-risk workers may lack either the information or liquidity to invest in retraining before a costly displacement. These cracks in the market suggest a role the government can potentially play in promoting and sponsoring training programs.
The track record in the United States for public worker training/retraining programs, however, is not encouraging. Participants often struggle to complete time-intensive and expensive retraining programs. Those who complete the programs struggle to recoup earnings lost during them. Meanwhile, state and federal worker training tax credits have generally been ineffective, largely due to low participation by firms.³
Public expenditures on labor market programs in the United States, such as worker training/retraining, are the second-lowest among OECD countries, comprising only 0.26% of GDP. Many European countries spend 2–3% of GDP on labor market programs (OECD, 2017), with stronger (although somewhat mixed) results.⁴
In pursuit of lifelong learning
Concerns about worker retraining issues and skill obsolescence precede recent concerns about the AI revolution. A common banner for these issues, which are particularly intense in Europe, has been the call for lifelong learning. Advocates see lifelong learning as a pathway for acquisition of skills that helps individuals adapt to new labor market demands and insulates them from the shocks of new technology. While there are many descriptions of the goals of lifelong learning and a wide range of approaches for firms and individuals, there is much less evaluation of the outcomes of existing programs, and there are relatively few empirically based conclusions about broadly successful approaches.
For example, as part of the EU2020 initiative, the European Commission proposed guidelines for lifelong learning programs that characterize effective lifelong learning programs as being flexible in where/how participants can access trainings⁵; focused on high-demand skills, such as literacy, numeracy, and digital skills; and offering regular feedback for the participants (European Commission, 2019). Similarly, analysis by a team at McKinsey identifies the integration of multimedia instructional methods, building training around specific skills required for on-the-job success, regular evaluation with feedback, and instruction adaptive to student strengths/weaknesses as some of the best practices in adult learning programs. The analysis describes the need to pair high-quality training with support services for participants, including compensation for lost or delayed wages, to ensure participants are able to complete the training program (McKinsey & Company, 2017). Presently, however, we are not aware of any evaluations of these or similar initiatives, nor of any case studies that could be instructive of how to implement these practices.
Many existing programs, meanwhile, rely on outmoded training methods. The bulk of on-the-job training consists of unstandardized experiential learning that is not easily evaluated and, therefore, poorly understood (McKinsey & Company, 2015), while many publicly sponsored training programs effectively provide training vouchers for community college courses (McCall et al., 2016). These models may be particularly ineffective for experienced workers who have spent many years learning on the job.
We are particularly interested in initiatives that proactively train workers at risk of future displacement. Waiting to train workers until they have been displaced increases both the cost (lost wages while unemployed) and challenge (more to learn to catch up to skills expected in labor market) of training. Moreover, existing research suggests that retraining programs for displaced workers generally do not pay off for participants (McCall et al., 2016). The alternative would seem to be continuing education and training throughout a worker’s career, particularly when concerns of skill obsolescence emerge (OECD, 2019). How to best implement such ideas on a broad scale for workers at the greatest risk of displacement remains an open question.
The individual side of the market
When discussing workforce training, the focus is largely on firm behavior, but individual workers are obviously a key element of any analysis of skills and adaptability. A large part of the long-run solution to the overarching concerns of technology-driven economic disruption will be the education individuals get before entry to the labor market. Over the next few decades, however, we must find solutions for the current workforce, whose formal schooling is (mostly) finished.
The existing evidence indicates that workers with more prior education make larger investments in training over their work life (Hanushek et al., 2017). Unfortunately, we have little evidence about the kinds of training currently employed workers receive, particularly training that involves developing new skills. There is substantial evidence about on-the-job training as it relates to firm-specific skills, but this work has said little about adaption to significant changes in skill demands akin to those coming from integration of new AI-intensive technologies.
Our initial questions in this area pertain to the kinds of training that facilitate transitions to new jobs, how heterogeneous skills development is, and how it might respond to varying incentives.
Firms are beginning to adopt some altered practices.
A number of large firms have announced efforts to integrate new training methods for existing employees. While the impacts of these new initiatives are currently unknown, they serve as interesting practical examples of some of the issues that we described above. As Walmart, the largest private employer in the United States, introduces shelf-scanning robots and truck-unloading conveyor systems, it is reorienting the role of its employees to complement the automation technology. In particular, Walmart is updating its employee training to shift focus from rote math and stocking skills that are more easily automated to softer, human-oriented skills, such as empathy and leadership.
In 2018, Walmart introduced the Live Better U tuition assistance program for full- and part-time employees. Eligible employees can pursue degree and certificate programs in fields that will help workers adapt to an automated workplace, including computer science and supply chain management. Participants receive full tuition support, application assistance, and degree coaching through Guild Education for a buy-in of $1 per day. The Live Better U program offers flexibility through online courses while proactively training employees in fields Walmart believes will be valuable in the next generation of its business model (Walmart, 2019). In its first year, 7,500 Walmart employees enrolled in the Live Better U program; Walmart expects as many as 68,000 employees to participate over the next ten years (Thomas, 2019). Two questions come out of this: How successful is the program in providing new skills, and how does the scale of the program compare to the relevant group of “endangered” employees?
Although only recently announced, the Amazon Upskilling 2025 initiative will be one of the first and most prominent large-scale implementations of many of the practices advocates of lifelong learning reform have proposed. Launched in July 2019, the Upskilling 2025 initiative pledged a $700 million investment through 2025 in a suite of training programs for Amazon’s existing workforce in skills complementary to its evolving business model. Amazon hopes to retrain at least a third of the 300,000 employees eligible to participate in the program. Many of the training programs will be designed by Amazon engineers. Participants will earn credit through an internal credentialing system that Amazon can use to transition non-technical employees into technical roles, including IT and software engineering (Amazon, 2019). Again, it will be important to consider how successful this program is and whether it provides a model for other firms.
In conclusion
Technological change brings both challenges and opportunities for firms and workers adapting to the economy of the future. While we expect technology to shift skill demand for firms, technology will also be a tool in training workers to develop these skills.
Neither firms nor individuals alone will achieve the socially optimal outcome: meeting the skill demands of the future economy without leaving anyone behind. It will almost certainly require mutual efforts of policymakers, researchers, education systems, entrepreneurs, and individuals to navigate the challenges on the horizon in the labor market. We aren’t there yet.
— —
Learn more about the Golub Capital Social Impact Lab at Stanford Graduate School of Business.
Follow us @GSBsiLab.
Written by Eric Hanushek, Lisa Simon and Jacob Light.
| 2020-04-23T00:00:00 |
2020/04/23
|
https://medium.com/@gsb_silab/the-research-challenges-of-the-ai-labor-market-challenges-272f69d98f9d
|
[
{
"date": "2019/12/17",
"position": 42,
"query": "AI economic disruption"
},
{
"date": "2019/12/17",
"position": 46,
"query": "AI economic disruption"
}
] |
How artificial intelligence will impact K–12 teachers
|
How artificial intelligence will impact K–12 teachers
|
https://www.mckinsey.com
|
[
"Jake Bryant",
"Christine Heitz",
"Saurabh Sanghvi",
"Dilip Wagle"
] |
New technologies for artificial intelligence in education could help teachers do their jobs better and more efficiently.
|
The teaching profession is under siege. Working hours for teachers are increasing as student needs become more complex and administrative and paperwork burdens increase. According to a recent McKinsey survey, conducted in a research partnership with Microsoft, teachers are working an average of 50 hours a week —a number that the Organisation for Economic Co-operation and Development Teaching and Learning International Survey suggests has increased by 3 percent over the past five years.
While most teachers report enjoying their work, they do not report enjoying the late nights marking papers, preparing lesson plans, or filling out endless paperwork. Burnout and high attrition rates are testaments to the very real pressures on teachers. In the neediest schools in the United States, for example, teacher turnover tops 16 percent per annum. In the United Kingdom, the situation is even worse, with 81 percent of teachers considering leaving teaching altogether because of their workloads. Further disheartening to teachers is the news that some education professors have even gone so far as to suggest that teachers can be replaced by robots, computers, and artificial intelligence (AI).
Our research offers a glimmer of hope in an otherwise bleak landscape. The McKinsey Global Institute’s 2018 report on the future of work suggests that, despite the dire predictions, teachers are not going away any time soon. In fact, we estimate the school teachers will grow by 5 to 24 percent in the United States between 2016 and 2030. For countries such as China and India, the estimated growth will be more than 100 percent. Moreover, our research suggests that, rather than replacing teachers, existing and emerging technologies will help them do their jobs better and more efficiently.
Our current research suggests that 20 to 40 percent of current teacher hours are spent on activities that could be automated using existing technology. That translates into approximately 13 hours per week that teachers could redirect toward activities that lead to higher student outcomes and higher teacher satisfaction. In short, our research suggests that existing technology can help teachers reallocate 20 to 40 percent of their time to activities that support student learning.
Further advances in technology could push this number higher and result in changes to classroom structure and learning modalities, but are unlikely to displace teachers in the foreseeable future. Many of the attributes that make good teachers great are the very things that AI or other technology fails to emulate: inspiring students, building positive school and class climates, resolving conflicts, creating connection and belonging, seeing the world from the perspective of individual students, and mentoring and coaching students. These things represent the heart of a teacher’s work and cannot—and should not—be automated.
Make no mistake, the value of a good education starts early and lasts a lifetime. Research suggests that simply having an effective kindergarten teacher can affect the likelihood of a student completing college thus boosting their lifetime earnings by about $320,000. Technology, when used correctly, can facilitate good teaching, but it will never replace teachers. In the remainder of this article, we will outline how teachers spend their time today, how technology can help to save teacher time, and where that additional time might go. Note that we are intentionally focused on the impact of technology on teacher time. In future articles we will address its broader impact on student learning.
How teachers spend their time
To understand how teachers are spending their time today and how that might change in a more automated world, we surveyed more than 2,000 teachers in four countries with high adoption rates for education technology: Canada, Singapore, the United Kingdom, and the United States. We asked teachers how much time they spend on 37 core activities, from lesson planning to teaching to grading to maintaining student records.
We asked where teachers would like to spend more and less time. We asked what technologies teachers and students were currently using in the classroom to discover new content, practice skills, and provide feedback. Finally, we asked what was working well and where they faced challenges, both in the application of technology and more broadly across their role as teacher. Our findings were unequivocal: teachers, across the board, were spending less time in direct instruction and engagement than in preparation, evaluation, and administrative duties (Exhibit 1).
How technology can aid teachers
Once we understood how teachers spend their time, we evaluated automation potential across each activity, based on an evaluation of existing technology and expert interviews. We concluded that the areas with the biggest potential for automation are preparation, administration, evaluation, and feedback. Conversely, actual instruction, engagement, coaching, and advising are more immune to automation (Exhibit 2).
Where to save time with technology
The area with the biggest automation potential is one that teachers deal with before they even get to the classroom: preparation. Across the four countries we studied, teachers spend an average of 11 hours a week in preparation activities. We estimate that effective use of technology could cut the time to just six hours. Even if teachers spend the same amount of time preparing, technology could make that time more effective, helping them come up with even better lesson plans and approaches. For example, several software providers offer mathematics packages to help teachers assess the current level of their students’ understanding, group students according to learning needs, and suggest lesson plans, materials, and problem sets for each group. In other subjects, collaboration platforms enable teachers to search and find relevant materials posted by other teachers or administrators.
Technology has the least potential to save teacher time in areas where teachers are directly engaging with students: direct instruction and engagement, coaching and advisement, and behavioral-, social-, and emotional-skill development. It is worth pausing here for a moment to note that we are not denying that technology will change the student experience of learning, although we would recommend caution and measured expectations.
While controlled pilot studies have shown improvements in student learning from technology-rich, personalized blended learning, these improvements have not yet been realized on a large scale. The most recent Program for International Student Assessment scores suggest that, globally, students who use tablets, laptops, and e-readers in the classroom are performing worse than those who do not. Why the disconnect?
Our hypothesis is that implementing technology in the classroom at scale is hard. Just providing hardware is easy. Integrating effective software that links to student-learning goals within the curriculum—and training teachers on how to adapt to it—is difficult. This underscores why we believe that technology in the classroom is not going to save much direct instructional time. To improve student outcomes, the teacher still needs to be in the classroom, but their role will shift from instructor to facilitator and coach. For example, some teachers are using flipped learning in their classrooms. Instead of teaching a concept in the classroom and then having students go home to practice it, they assign self-paced videos as homework to give the basic instruction and then have students practice in the classroom, where the teacher can provide support and fill gaps in understanding.
How to improve student educational outcomes
Evaluation and feedback complete the teaching loop. As teachers understand what their students know and can do, they can then prepare for the next lesson. Technology has already helped here—for example, computer grading of multiple-choice questions was possible long before AI and is particularly penetrated in math instruction. More is possible. Advances in natural-language processing make it possible for computers to assess and give detailed, formative feedback across long-form answers in all subject areas. For example, writing software can look at trends in writing across multiple essays to provide targeted student feedback that teachers can review and tailor. Combined, these technologies could save three of the current six hours a week that teachers spend on evaluation and feedback.
Finally, administration is a bugbear of teachers globally. After all, who prefers filling out paperwork to interacting with children? Good news is on the horizon. Automation could reduce the amount of time teachers spend on administrative responsibilities—down from five to just three hours per week. Software can automatically fill out forms (or provide menus of potential responses); maintain inventories of materials, equipment, and products; and even automatically order replacements.
Where the time will go
What will teachers do with the additional 13 hours a week saved by the application of technology? Some of this time, hopefully, will be given back to teachers themselves—to spend time with their families and their communities—thus increasing the attractiveness of teaching as a profession.
Much of the time saved, however, can be plowed back into improving education through more personalized learning and more direct coaching and mentoring. In our survey, about a third of teachers said that they wanted to personalize learning but did not feel that they were doing so effectively at present. Their biggest barriers: time, resources, materials, and technology (Exhibit 3). Automation can help with all of these. Even when teachers believed that they were already providing tailored materials—and personalized feedback—to students, students often disagreed. While 60 percent of the teachers surveyed believed that their feedback was personalized to each student, only 44 percent of the students surveyed felt the same way.
Additional time can also help support social–emotional learning and the development of the 21st-century skills that will be necessary to thrive in an increasingly automated workplace. It will enable teachers to foster one-on-one relationships with students, encourage self-regulation and perseverance, and help students collaborate with each other. Research shows that strong relationships with teachers promote student learning and well-being, especially for students from low-income families. Automation within the teaching profession could thus be a catalyst in reducing educational inequalities.
Finally, teachers could spend more time collaborating with each other. More time for collaboration should translate into better outcomes for students. International comparative studies show that high-performing school systems double down on peer coaching and collaborative lesson planning. These practices can support teachers in improving and developing their craft. For example, the leerkRACHT Foundation in the Netherlands has introduced peer collaboration into 10 percent of Dutch schools, with 80 percent of teachers reporting improvement in student learning.
How to make it happen
All of this begs a question: How will we capture the promise of technology in our schools? The good news is that this is not about technology we have not yet invented. It will not require AI systems that pass the Turing test. To the contrary, achieving these savings in teacher time is mostly about adoption of existing education technology. Just bringing the average school to the level of the best would have a huge impact.
This, however, is no small task. It will require commitment across a broad range of stakeholders, including governments, school leaders, technology companies, and, of course, teachers and learners themselves. Four imperatives stand out as schools move to adopt technology wisely: target investment, start with easy solutions, share what is working, and build teacher and school-leader capacity to harness technology effectively.
The schools that are currently best in applying technology to save teacher time have often been able to access more funding than the average school. Democratizing these gains will entail increased investment in every school, especially those that are currently under-resourced. As investment increases, it will be critical to target it to the areas that can most effectively save teacher time and improve student outcomes (rather than to flashy but ineffective hardware).
Starting with easy solutions will provide early momentum. Proven technology that can replace simple administrative tasks or simple evaluative tools for formative testing can immediately provide teachers with respite, whetting their appetite for more holistic solutions.
Part of the problems that schools face today is the myriad of competing solutions, some of which are fantastic, but many of which promise great things but deliver little. Sharing what is working—and what is not working—is therefore critical. Neutral arbiters bringing objective and rigorous performance data, similar to the service that EdReports.org provides on curriculum, are necessary in the education-technology space. It will also be necessary to make best-practice solutions available to teachers at all types of schools and school systems.
Finally, building the capacity of teachers and school leaders to harness technology effectively will ensure maximum gains in not only saving teacher time but also improving student outcomes. Districts and schools need to balance introducing new technologies with fully integrating existing ones into the curriculum and teachers’ professional development. Districts need to use accepted, widely adopted tools for consistency. However, teachers should have the freedom to pilot alternatives, and they should have a strong voice in deciding which tools are working in the classroom and should roll out districtwide. Technology companies, too, need to be better in including the voice of the teacher when guiding product development.
If these four imperatives are met, then we are hopeful that automation will be a boon and not a bane for teachers. Ten years from now, with the support of a range of education technologies, teachers should have more time for themselves—and more time for their students. They can pour that time into improving student outcomes and preparing students for a more challenging and automated workforce.
| 2020-01-14T00:00:00 |
https://www.mckinsey.com/industries/education/our-insights/how-artificial-intelligence-will-impact-k-12-teachers
|
[
{
"date": "2020/01/14",
"position": 35,
"query": "artificial intelligence education"
},
{
"date": "2020/01/14",
"position": 35,
"query": "artificial intelligence education"
},
{
"date": "2020/01/14",
"position": 37,
"query": "artificial intelligence education"
},
{
"date": "2020/01/14",
"position": 34,
"query": "artificial intelligence education"
},
{
"date": "2020/01/14",
"position": 38,
"query": "artificial intelligence education"
},
{
"date": "2020/01/14",
"position": 38,
"query": "artificial intelligence education"
},
{
"date": "2020/01/14",
"position": 35,
"query": "artificial intelligence education"
}
] |
|
Retraining and Reskilling for AI – The State of AI Job Loss Today
|
The State of the Enterprise Today
|
https://emerj.com
|
[] |
Learn how business leaders can think about the potential for layoffs due to AI automation and what to do about retraining and reskilling ...
|
One of the many concerns that business leaders have around automation is how they're going to adapt their workforce to new technology. The digital age has brought a rapid shift in the skillsets employees need to go about their jobs effectively, leaving little time for business leaders to come up with strategies for how to reskill and retrain their employees, prevent their companies from drawing disdain from the public, and how to compete with their peers.
The era of AI disruption has begun, and with it, many large companies are making press releases on how they've launched a new AI initiative—...
| 2020-01-20T00:00:00 |
https://emerj.com/retraining-and-reskilling-for-ai/
|
[
{
"date": "2020/01/20",
"position": 84,
"query": "reskilling AI automation"
},
{
"date": "2020/01/20",
"position": 82,
"query": "reskilling AI automation"
},
{
"date": "2020/01/20",
"position": 94,
"query": "reskilling AI automation"
}
] |
|
A.I. is transforming the job interview—and everything after - Fortune
|
A.I. is transforming the job interview—and everything after
|
https://fortune.com
|
[
"Maria Aspan"
] |
Hiring is where A.I. currently is most widely used in personnel management. In this arena, “artificial intelligence” often gets lumped ...
|
This article is part of a Fortune Special Report on Artificial Intelligence.
In his Amsterdam offices, about an hour’s drive from his company’s largest non-American ketchup factory, Pieter Schalkwijk spends his days crunching data about his colleagues. And trying to recruit more: As head of Kraft Heinz’s talent acquisition for Europe, the Middle East, and Africa, Schalkwijk is responsible for finding the right additions to his region’s 5,600-person team.
It’s a high-volume task. Recently, for an entry-level trainee program, Schalkwijk received 12,000 applications—for 40 to 50 openings. Which is why, starting in the fall of 2018, thousands of recent university graduates each spent half an hour playing video games. “I think the younger generation is a bit more open to this way of recruiting,” Schalkwijk says.
The games were cognitive and behavioral tests developed by startup Pymetrics, which uses artificial intelligence to assess the personality traits of job candidates. One game asked players to inflate balloons by tapping their keyboard space bar, collecting (fake) money for each hit until they chose to cash in—or until the balloon burst, destroying the payoff. (Traits evaluated: appetite for and approach to risk.) Another measured memory and concentration, asking players to remember and repeat increasingly long sequences of numbers. Other games registered how generous and trusting (or skeptical) applicants might be, giving them more fake money and asking whether they wanted to share any with virtual partners.
Their results, measured against those of games played by 250 top-performing Kraft Heinz staffers, told Schalkwijk which candidates Pymetrics thought were most likely to succeed—because their traits, as represented by their gaming skills, most closely matched those of the risk-seeking, emotionally intelligent employees the company prizes. That data in turn helped decide job offers, creating a machine-assisted recruiting class.
Schalkwijk is one of a fast-growing cohort of human resources executives relying on artificial intelligence to recruit, assess, hire, and manage their staff. In a 2018 Deloitte survey, 32% of business and technology executives said they were deploying A.I. for “workforce management.” That share is almost certainly higher today—and it’s spreading to encompass some of the world’s largest companies.
A machine-learning algorithm is like a toddler; it will learn from its environment. We haven’t had a diverse group at the table creating this technology to date.
FRIDA POLLI, COFOUNDER AND CEO, PYMETRICS
As a job seeker, you might have your application vetted by a Mya Systems chatbot at L’Oréal or PepsiCo. You could respond to an A.I.-crafted job posting vetted by Textio, perhaps at Expedia Group or ViacomCBS. You could be asked to play Pymetrics games not only at Kraft Heinz but also at Unilever or JPMorgan Chase. You could record one of the automated HireVue video interviews used by Hilton and Delta Air Lines.
Your relationship with A.I. may extend past the job offer too. Once hired, you might find yourself filling out employee-engagement surveys designed by Microsoft’s LinkedIn, where your answers could help set your manager’s performance targets. Your employer could tap you for promotion opportunities identified by Workday’s A.I. If you work at an Amazon warehouse and miss your productivity goals, in-house systems could recommend that you be fired. On the other hand, if you work at IBM and plan to quit, in-house systems might guess your plans and warn your managers that they should try to make you happy.
Companies are delegating considerable responsibility to these machines, and the list of personnel tasks in which A.I. plays a role is likely only to grow. Low unemployment and tight labor markets are putting employers under pressure to take any technological advantage they can get in the war for talent.
In a LinkedIn survey of hiring managers and recruiters who use A.I., 67% said they embraced the tech because it helped them save time. And a smaller cohort, 43%, cited an arguably more important motivation: A.I., they said, would help them combat bias in their decision-making. “People are inherently biased,” says Schalkwijk. “I wanted less biased hiring decisions and more data-driven hiring decisions.”
At its best, its creators and adopters argue, A.I. can eliminate bias from the hiring process. This can foster greater gender and racial diversity—both of which are associated with better business performance and employee engagement. A.I. can also purportedly look past another kind of bias, providing more opportunities to applicants who don’t have expensive brand-name educations. Before using Pymetrics, Kraft Heinz recruiters tended to scan résumés looking for top-tier universities. Now, Schalkwijk says, “it doesn’t matter if you’re from Cambridge.”
More broadly, A.I. can help employers better perceive their workers’ strengths. Contenders including LinkedIn and enterprise-cloud specialist Workday have built A.I.-enabled tools that they say can help human managers better recognize or track employees’ skills. “We can use technology to find patterns that I wouldn’t as a team leader be able to find in the past, to coach and develop people in a more thoughtful way,” says Greg Pryor, a senior vice president overseeing Workday’s internal talent-management programs. (In addition to selling it, Workday uses this technology with its own employees.)
Still, for all its potential, many employers are approaching A.I. warily. They’re confronting the promise-and-peril irony of applying A.I. to human populations: Done correctly, it has the potential to eliminate bias and discrimination; done injudiciously, it can amplify those same problems. And in a new, very much unregulated market, such problems may be hard to spot until it’s too late. Even some executives who are using A.I. express skepticism in private about what the technology can do—or what its drawbacks might be.
“We’re in sort of the primordial ooze of how A.I. is going to find its way,” says Gordon Ritter, founder of venture firm Emergence Capital and an investor in several A.I. startups. “Is it friend or foe?” Ritter is betting that A.I. will prove beneficial, but for now, to many executives, the ooze still looks murky.
“A.I. is like teenage sex,” says Frida Polli. “Everyone says they’re doing it, and nobody really knows what it is.”
The joke has been making the rounds in A.I. circles for a while, and Polli, the cofounder and CEO of Pymetrics, has been around long enough to see the truth in it. After getting a Ph.D. in neuropsychology and working in Harvard and MIT research labs, Polli found herself divorced, supporting a young daughter, and burned out on academia’s low paychecks. She went back to Harvard for business school, and in 2013 she started a cognitive assessment company with a former MIT colleague. Pymetrics promises to help employers make better, more diverse hires, based on what applicants could do rather than what their résumé says or what college they graduated from. The venture-funded New York startup now has a valuation of $190 million, according to PitchBook, and between $10 million and $20 million in annual revenues; its games are used by about 100 employers.
A fierce A.I. evangelist, whose clear blue eyes and near-platinum hair match the intensity of her conversational speed, Polli acknowledges—and parries—critiques of the technology’s potential for misuse. Yes, bad A.I. actors exist, she says. But it’s not like humans are so much better, as demonstrated by enduring gender, racial, and class disparities. “There’s a front door to hiring and a back door,” Polli argues, “and the front door’s broken.”
Frida Polli’s startup, Pymetrics, designs games that work in conjunction with A.I. to assess job candidates’ personality traits. She says the system helps companies make more diverse hires—and, consequently, perform better. Photograph by DeSean McClinton-Holland for Fortune
Hiring is where A.I. currently is most widely used in personnel management. In this arena, “artificial intelligence” often gets lumped together with basic automation, such as keyword searches of résumés. But it more specifically refers to machine learning—where software teaches itself about correlations between applicants’ backgrounds and behavior and their potential performance.
The problem, notes Matissa Hollister, an assistant professor of organizational behavior at McGill University, is that a machine-learning system is only as unbiased as the information it learns from. “To the extent that the real world contains bias,” she says, “there’s the risk that the algorithm will learn that bias and perpetuate it.”
That has already happened in some prominent cases. Amazon spent years building a résumé-analysis algorithm—one that it never used, because it turned out it discriminated against women. Because most of the previously submitted résumés it assessed were from men, the algorithm taught itself that men were always preferable hires.
More recently, HireVue, which uses A.I. to vet video interviews, has drawn scrutiny around bias issues. HireVue’s system asks applicants to use smartphone or laptop cameras to record answers to automated questions; its software then analyzes factors including word choice and facial expression. The Utah-based vendor, majority-owned since October by private equity firm the Carlyle Group, introduced its facial-analysis product in 2014. It has since been used by roughly 100 employers to assess more than 1 million applicants.
Its use hasn’t gone uncriticized. A.I. that relies on facial recognition can often misidentify or misread faces of color, especially those of darker-skinned women. HireVue says that its facial-analysis technology doesn’t extend to facial recognition. But a prominent privacy watchdog has asked the Federal Trade Commission to investigate HireVue for “unfair and deceptive practices”—challenging its use of facial analysis and of algorithmic assessments that are not transparent.
HireVue CEO Kevin Parker downplays the importance of facial analysis to HireVue’s assessments, and he argues that his company is “very focused on eliminating bias.” By standardizing how candidates are assessed, he argues, HireVue provides a superior alternative to ordinary hiring. “It’s certainly better than the typical ‘I know it when I see it’ ” snap judgment, he says.
But the criticism HireVue faces points to the problem highlighted by Hollister: Machines are as likely to amplify biases as they are to sidestep them. That’s especially problematic when the people designing the tools are predominantly white and male, as is the case in much of the tech industry. “A machine-learning algorithm is like a toddler; it will learn from its environment,” Polli says. “We haven’t had a diverse group at the table creating this technology to date.”
Equally unsettling to labor advocates is that most A.I. technology is both unregulated and opaque to the workers affected by it. Employers and vendors have to comply with antidiscrimination guidelines from the Equal Employment Opportunity Commission, but the EEOC has no A.I.-specific rules. Illinois recently passed a law that requires disclosure when employers use automated video interviewing. Industry members and critics agree it’s a good first step—but only a first step.
“We may not have proof of bias. We also don’t have proof of benevolence,” says Meredith Whittaker, a former Google employee and cofounder of the AI Now Institute at New York University. A.I.-enabled hiring systems are “sold to employers, not to workers,” she points out.
Even so, employers are still figuring out whether A.I. will advance those interests. Most have been using A.I. in human resources for only a few years, if that. “It’s a trend that’s here to stay,” says Ifeoma Ajunwa, an assistant professor at Cornell who studies automation in hiring. “But A.I.’s still a blunt tool.”
In a tower at the heart of Times Square, with remnants of New Year’s Eve crowds still dispersing from the streets below, Eric Miller is talking back to his computer. It doesn’t love what he’s typed. “It’s currently ‘comparing this writing to 102 million job posts.’ So thank you for that,” Miller snarks, mock-offended. A few minutes later, a different bit of writing passes machine muster: “It liked me! That’s a first.”
Of course, Miller is one of the people who invited this critic into his company in the first place. He’s the vice president of global talent acquisition for ViacomCBS, and he’s scanning through Viacom’s library of more than 200 A.I.-assisted job listings. For the past year his team has fed these listings through A.I. technology produced by startup Textio. A Seattle-based company founded by Microsoft veterans, Textio makes what’s essentially a woke word processor.
Textio’s program compares job listings and other communications with those written by other employers throughout its system (hence those 102 million other posts). The machine-learning technology measures the response that different posts attract, and from whom, and constantly assesses whether certain words and phrases attract or repel candidates—owing to subtle linguistic bias or just plain bad writing.
HR is already looked at as ‘those people,’ the bad guys, right? If you start to introduce something that feels mechanical and employees pick up on that, that’s not a good look.
ERIC MILLER, VP OF GLOBAL TALENT ACQUISITION, VIACOMCBS
In the job description Miller is working on, the word “expert” is highlighted in light blue, to signify that it conveys a slightly masculine tone; swapping in “authority” makes the language more gender neutral. Loaded terms like “aggressive” are out, even though Miller may want recruits who can “meet aggressive deadlines.” (“You probably don’t think about that,” he explains, “but Textio thinks a lot about it.”) The software even flags corporate jargon like “drive results,” which can turn off potential applicants; Textio prefers asking them to “get results.”
Which ViacomCBS is doing. The company has seen a 28% increase in applications to jobs whose descriptions Textio rates as “neutral” in tone and is filling jobs with high Textio scores 11 days faster, Miller says. It’s seeing a measurable increase in gender diversity among applicants, too, including in traditionally male-dominated engineering roles.
It all seems like a benign first step in bringing A.I. into the HR process. Yet ViacomCBS has taken about a year to roll it out. And Miller has words of caution for fellow human resources executives who want to embrace A.I. “HR is already looked at as ‘those people,’ the bad guys, right?” he says. “If you start to introduce something that feels mechanical and employees pick up on that, that’s not a good look.” His advice for a better look? “Do your research. Check. Check again.” Miller’s biggest piece of advice echoes that of academics and critics: Make sure you or your vendors conduct regular audits, ideally by independent third parties, to ensure that the A.I. itself isn’t discriminating against specific groups.
But who exactly are the auditors? Cornell’s Ajunwa foresees a day when an independent agency gives out “fair automated hiring” certifications. For now, though, audits are largely self-imposed. Polli says she has an academic auditor lined up for Pymetrics and is in talks with a second; HireVue’s Parker says he hopes to hire an auditor by the end of March.
It all adds up to the kind of gray area that makes corporate legal departments nervous. “You have to be methodical about [A.I.], or you’re going to be doing damage,” Miller says. “But the rewards are huge if you get it right.”
A.I. providers haven’t proved that those rewards translate into bottom-line gains—but they say that day is coming. Pymetrics, for one, claims its technology can lead to better overall business performance. According to an anonymized case study provided by Polli, one insurance customer found that sales employees who had been “highly recommended” by Pymetrics generated 33% more annual sales than other hires.
In Amsterdam, Pieter Schalkwijk is measuring rewards by other metrics. Kraft Heinz has been able to hire talent with a broader mix of expertise: Before implementing the Pymetrics tests, about 70% of trainee hires had business degrees. Last year, only about half did, and around 40% had engineering degrees. Kraft Heinz has been so pleased with early results, Schalkwijk says, that it’s using Pymetrics tests in some U.S. hiring efforts.
Still, he too is proceeding cautiously. For example, Kraft Heinz will likely never make all potential hires play the Pymetrics games. “For generations that haven’t grown up gaming, there’s still a risk” of age discrimination, Schalkwijk says.
He’s reserving judgment on the effectiveness of Pymetrics until this summer’s performance reviews, when he’ll get the first full assessment of whether this machine-assisted class of recruits is better or worse than previous, human-hired ones. The performance reviews will be data-driven but conducted by managers with recent training in avoiding unconscious bias. There’s a limit to what the company will delegate to the machines.
A.I. “can help us and it will help us, but we need to keep checking that it’s doing the right thing,” Schalkwijk says. “Humans will still be involved for quite some time to come.”
Five ways that A.I. is remaking the workplace
More companies are relying on artificial intelligence (often created by nimble startups) to help with the more time-consuming and complex elements of finding and managing talent. Here are five arenas where A.I.’s role is growing.
1. Chatbot recruiters
These tools are aimed at big employers seeking to hire part-time or low-wage employees en masse: Think call centers, or retailers staffing up seasonally. A.I.-enabled chatbot Mya Systems helps clients including L’Oréal and PepsiCo do vetting and interview-scheduling.
2. Deep background checks
Think twice about that Tweet. Fama Technologies uses A.I. to analyze the social media feeds of potential hires and current employees, looking for signs of racism, misogyny, or toxic behavior. Checkr provides general A.I.-enabled background checks for employers including Uber and Lyft.
3. Employee advisers
More companies are deploying A.I. to monitor and help people they’ve already hired. Workday is rolling out technology to track workers’ skills (and proactively offer them chances for advancement); talent-acquisition startup Eightfold.ai says its similar platform can reduce unwanted attrition by 25%.
4. Management coaches
As employers try to improve employee engagement, many are enlisting A.I. to figure out and fix what’s wrong. Technology from Microsoft’s LinkedIn regularly surveys employees; it then flags a decline in morale or unusual underperformance and offers suggestions about how managers could improve.
5. Performance (review) artists
Employers have begun to introduce more A.I. into what remains a largely human-driven process. LinkedIn in September launched a product that allows employers and workers to check in on performance goals and feedback more regularly (and to compare accomplishments across an entire company).
A version of this article appears in the February 2020 issue of Fortune with the headline “Siri, Did I Ace the Interview?”
More from Fortune’s special report on A.I.:
—Inside big tech’s quest for human-level A.I.
—A.I. breakthroughs in natural-language processing are big for business
—Facebook wants better A.I. tools. But superintelligent systems? Not so much.
—A.I. in China: TikTok is just the beginning
—Medicine by machine: Is A.I. the cure for the world’s ailing drug industry?
Subscribe to Eye on A.I., Fortune’s newsletter covering artificial intelligence and business.
| 2020-01-20T00:00:00 |
https://fortune.com/longform/hr-technology-ai-hiring-recruitment/
|
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Rising Importance: Retraining Employees for AI & Automation
|
Rising Importance: Retraining Employees for AI & Automation
|
https://offeracceptstaffing.com
|
[
"Farley Ashby",
"Farley Ash"
] |
But reskilling and employee training shouldn't be relegated to a mere requirement to meet the rapidly changing demands of the modern workplace.
|
The need for continual investment into employee training and reskilling has never been greater. According to a recently published IBM report, 120 million workers in the world’s 12 largest economies will need to be reskilled over the next three years as a result of automation and AI-driven innovation alone. That’s before even mentioning the fact that 7 million open positions were left unfilled in America due to an inability to find employees with the necessary skills.
But reskilling and employee training shouldn’t be relegated to a mere requirement to meet the rapidly changing demands of the modern workplace. Effective employee retraining and reskilling can also lead to considerable gains in retention rates and overall business performance. There’s a reason that the world’s leading CEOs rank investment in people as the number one way to accelerate performance. Let’s take a look at why this is the case.
The Best Retention Strategy Is Continuous Employee Training
Today’s employees have never valued continuous training as much as they do today. With three-quarters of the workforce set to be made up of the millennial generation by 2025, forward-thinking employers are changing their approach to match employee demands.
With 87% of millennial workers rating continued professional training as very important in their job considerations, companies that neglect their in-house training will lose their top talent to those with comprehensive training programs. In a world where “portfolio” careers are now the norm, you have to provide much more than a paycheck to stimulate loyalty.
Furthermore, by making that investment into employees, businesses are able to demonstrate that they value their employees. Workers will enjoy being challenged through training opportunities, even if the subject material covers core competencies. Research shows that 40% of employees who don’t receive the necessary job training to become competent in their role will leave their position in the first year. With the cost of replacing a new hire placed at $30,000 by Oxford Economics, the average training spend of $1,500 per employee is a drop in the ocean by comparison.
Therefore, by making a concerted effort to improve your in-house training programs, your company will be able to reap the rewards of an engaged, happy, and most importantly, loyal workforce.
Retraining and Reskilling Provide a Competitive Advantage
Reskilling employees shouldn’t just be an exercise in reducing employee turnover, however. Many businesses have already recognized the potential to improve business performance through continual investment in employee development.
The figures demonstrate this to be the case. A study from the American Society for Training and Development (ASTD) uncovered that companies with comprehensive training programs earned 218% higher income per employee than those without formalized training. Better still, those companies also enjoyed a 24% higher profit margin than competitors who skimped on training investments. So what are the driving forces behind such dramatic results?
Firstly, training considerably increases employee productivity. At some companies, individuals never receive the level of training required to perform their role optimally. However, those who continue to invest in skills advancements witness significant leaps in productivity as employees are better able to adapt to the changing demands of their role and utilize new methodologies to achieve faster results.
Fascinatingly, some of these skills advancements are fundamental in nature. According to a study carried out by the Warwick Institute for Employment Research, one in ten employees lacks entirely the digital skills required to carry out their duties. This is because companies forget that – in our increasingly digital economy – retraining is necessary for even the most basic of concepts. For instance, look at how simple Microsoft products (such as Windows, Word, and Excel) have all changed over the last decade. Yet many employees are operating without any knowledge of how to take advantage of their latest capabilities.
Another facet of improved business performance through retraining and reskilling is agility and flexibility. As corporate structures become flatter, and roles and responsibilities more varied, each employee will increasingly become a “jack of all trades.” By doing so, companies can quickly react to increases in demand, pivot to new markets in turbulent economic conditions, and reduce hiring spend. Those who overlook reskilling their employees could be left with an incompetent workforce left behind by market innovation.
Automation and Artificial Intelligence Are About to Change the Labor Market Permanently
The most prominent driving force behind the need to retrain and reskill employees is automation technology driven by Artificial Intelligence (AI). The figures are stark. Up to 1.4 million American workers will need to be reskilled by the year 2026, with 70% of those needing to do so because their job will cease to exist. What’s more, by 2030, 15% of the global workforce will have their job function carried out by an autonomous machine.
When this scenario comes to pass, 82% of executives at firms with a turnover of over $100 million believe that retraining and reskilling will account for over half of the answer to the resulting skills gap. Forward-thinking companies are beginning the retraining and reskilling process now to facilitate a smoother transition and to steal a march on competitors that have been slow to react.
Those that invest little in retraining and reskilling over the coming decade will quickly feel the burden of trying to cover their employee skills gaps with costly hiring processes, which could ultimately result in financial ruin. Therefore, it makes sense to start building a retraining and reskilling program for your employees today, to ensure you can compete as a business from 2030 onwards.
Employers Need to Implement a Retraining and Reskilling Program or Face the Consequences
Retraining and reskilling your employees offers minimal downside and a huge upside. And yet, many companies continue to overlook opportunities to introduce programs that could lower employee turnover, improve business performance, and help the transition into a more autonomous economy.
In a market where millions of job positions are left unfulfilled, as an employer, you need to focus on retaining your highest-performing talent as well as getting the most out of them by providing them with the skills they need to perform their roles. Furthermore, with technological innovation set to alter the landscape of the global economy permanently, your company cannot afford to be slow to react. Employees without the necessary skills to thrive will leave your company on the edge of collapse as you scramble to hire the knowledge you need to survive.
Here at Offer Accept, we can provide businesses with advice on how to beat the impending skills gap while providing access to talented individuals equipped with the skills to navigate the technological innovation taking place over the coming decade. Just give us a call on 305-910-2524 to discuss your recruiting needs with an expert.
Farley Ashby
Founder, Offer Accept
Recruiting Expert
| 2020-01-28T00:00:00 |
2020/01/28
|
https://offeracceptstaffing.com/the-importance-of-retraining-and-reskilling-employees-with-ai-and-automation-on-the-rise/
|
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Three Ways AI Can Discriminate in Hiring and Three Ways Forward
|
Three Ways AI Can Discriminate in Hiring and Three Ways Forward
|
https://www.urban.org
|
[
"Jenny R. Yang"
] |
AI may be able to help employers identify workers who have been excluded from traditional pathways to success but have the skills necessary to succeed.
|
In 2012, college engineering student Kyle Behm applied for a number of hourly jobs at retail stores. Behm had worked in similar positions, but the jobs he applied for required personality assessments. Kyle had been diagnosed with bipolar disorder, so questions about whether he experienced mood changes led many of the retailers to reject him even though he was well qualified.
Behm’s story illustrates the risks posed by a new generation of tools powered by artificial intelligence (AI) that are transforming the lives of America’s workers, with profound implications for civil rights.
Last week, I testified before the House Committee on Education and Labor Subcommittee on Civil Rights and Human Services to discuss how technology is changing work and how policymakers can address the new civil rights challenges raised by algorithmic hiring tools, worker surveillance, and tech-enabled business models that disrupt traditional employer-employee relationships.
Many new tech-driven hiring systems use AI to more quickly filter through increasing numbers of online applicants. Employers are using chatbots, résumé-screening tools, online assessments, web games, and video interviews to automate various stages of the hiring process.
Some employers aim to hire more quickly, assess “cultural fit,” or reduce turnover. Others aim to make better job-related decisions and hire more diverse candidates, expanding the applicant pool by measuring abilities rather than relying on traditional proxies for talent, such as graduation from an elite university, employee referrals, or recruiting from competitors. AI may be able to help employers identify workers who have been excluded from traditional pathways to success but have the skills necessary to succeed.
But with AI, machines work to replicate human decisionmaking. Often the bias in AI systems is the human behavior it emulates. When employers seek to simply automate and replicate their past hiring decisions, rather than hire based on a rigorous analysis of job-related criteria, this can perpetuate historic bias. Discriminatory criteria can be baked into algorithmic models and then rapidly scaled.
Bias may enter AI-powered systems in at least three ways:
1. Biased data. Data used to train algorithms may introduce bias. Amazon’s effort to build a résumé-screening tool highlights this challenge. Amazon’s model—trained on 10 years of résumés submitted primarily by men—learned to penalize women applicants.
2. Biased variables. Variables considered by algorithms often contain bias, and models may learn to use proxies for protected characteristics. For example, zip codes can be a proxy for race. Selecting biased variables can reflect developers’ blind spots—an acute concern considering the lack of diversity in the field.
3. Biased decisions. Humans may misuse models’ predictions and place undue weight on them, leading to discriminatory decisions.
Compounding these problems, many systems operate as a “black box,” meaning vendors of algorithmic systems do not disclose how inputs lead to decisions. Systems may rely on inaccurate or biased data and may not be designed to enable anyone to understand or explain a particular hiring decision. Because technology provides a sense of objectivity and scientific analysis, employers may not question automated, essentially unreviewable, decisions.
The answer to these concerns is not to simply return to human decisionmaking. Subjective decisionmaking practices have long perpetuated discrimination while being very difficult to challenge.
Used appropriately, technology can serve as a tool to support data-driven efforts to measure how bias operates at different stages of the employment process. Technology can help employers learn when and how bias occurs—whether in the recruitment phase, résumé review, the interview process, or deciding pay and promotions. Algorithms can play a powerful role in improving decisionmaking by identifying job-related criteria and behaviors, as well as patterns of hidden bias.
To harness AI’s potential, we need to ensure robust safeguards to address the new risks of AI systems. I share three strategies to chart a way forward:
1. Ensure a third-party audit of the development and use of AI tools.
A third-party auditing system would promote accountability by employers and vendors while having flexibility to evolve with technology and protect intellectual property. The government has an important role in creating an auditing framework and core requirements for retention and documentation of technical details, including disclosing training data for review during an investigation.
Independent auditors could follow established principles in the computer science and test validation fields, informed by workers, civil rights principles, and the public. This would promote meaningful transparency and external review while enabling standards to adapt with technological advances.
2. Adopt a workers’ bill of rights.
A workers’ bill of rights for algorithmic decisions would ensure understanding of how decisions are made and would provide a process to challenge biased or inaccurate decisions. These four areas are an important starting place:
1) Notice and consent: Workers should have the right to know and consent to the information collected to screen and evaluate them and to understand how personal information is stored, sold, or otherwise used. Employees need to understand how they will be evaluated so they can determine whether they need to seek reasonable accommodation for a disability under the Americans with Disabilities Act or otherwise have reason to believe the automated screen may be inaccurate.
2) Right to an explanation: To address concerns about fairness and accuracy, employers should explain the information considered for an applicant and the rationale for a decision in terms that a reasonable worker could understand.
3) Process for redress: Workers should have the right to view the data collected on them and have an opportunity to correct errors through an accessible process with human review and redress for harms.
4) Accountability: Employers and vendors have a responsibility to ensure systems are auditable by third parties, including in litigation or a government investigation. This includes retaining records on data used to train algorithms, as well as documentation of decisions made by algorithmic systems.
3. Update existing federal guidelines.
An update to the 1978 Uniform Guidelines on Employee Selections Procedures would provide valuable guidance on the validation standards for algorithmic screens. A revision could incorporate the latest scientific understanding into unified government principles.
To ensure a future that advances equal opportunity, it is essential that we have robust interdisciplinary engagement and public participation in the creation of safeguards that create meaningful accountability. The Urban Institute has been facilitating cross-sector dialogue, including through Urban’s May 2019 Knowledge Lab on Artificial Intelligence and Employment Equity and an October 2019 convening Urban hosted in collaboration with Upturn, the Leadership Conference on Civil and Human Rights, and the Lawyers’ Committee for Civil Rights Under Law.
By bringing together lawyers, employers, tech developers, computer and data scientists, and industrial and organizational psychologists and other social scientists, we are exploring strategies for ensuring fairness and equity in the use of hiring algorithms and AI.
| 2020-02-12T00:00:00 |
2020/02/12
|
https://www.urban.org/urban-wire/three-ways-ai-can-discriminate-hiring-and-three-ways-forward
|
[
{
"date": "2020/02/12",
"position": 55,
"query": "artificial intelligence hiring"
}
] |
The Impact of AI and Machine Learning on Workforce Management
|
The Impact of AI and Machine Learning on Workforce Management
|
https://www.sutisoft.com
|
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AI and ML have enhanced every stage of the hiring process by equipping HR teams with personalized research tools to find the best talent in the ...
|
Human resource management has experienced significant changes in the last few years due to the evolution of technologies. Today, Artificial Intelligence (AI) and Machine Learning (ML) are reshaping the way organizations recruit, manage, and engage with their employees.
The latest data-driven technology is rapidly making its way into the workforce management as companies are focusing more on creating an employee-oriented work culture.
Recruitment is no more a tiresome process for HR teams as it no longer entails lengthy activities such as manually screening the resumes of the applicants, making phone calls or replying to candidates via emails.
These routine tasks are now managed by smart technologies designed to replace human conversation, thus enabling HR teams to think about the bigger picture.
Employee engagement is essential for every business because it plays a major role in improving productivity and helping companies stay competitive in the market. Gone are the days when HR teams relied on troublesome annual surveys to evaluate employee engagement that often generated invalid results.
To redefine performance management, HR teams can count on real-time data to measure employee engagement and find trouble areas to enhance work culture by predicting workforce trends. Additionally, real-time data enables HR professionals to take quick action in a personalized manner.
AI and ML, the new trends in technology, have a significance in HR management practices. AI breaks down and modifies data into a format that is easy to read, and ML scans data to spot patterns and modifies plan actions correspondingly.
The insights provided by AI and ML generate suitable data to help HR recruiters retain and motivate existing employees and also to hire new job aspirants. Additionally, AI and ML-powered suggestions utilize historical data for recommending the best solutions to resolve expected problems, thus helping HR professionals develop HRM programs based on smart data.
A few advantages of using AI and ML in HR management:
Reduces Biased Appraisals
The crucial challenge that HR managers face during performance appraisals is to stay unbiased. AI/ ML algorithms go beyond spreadsheet analysis by executing employee assessments via regular, fair performance appraisals. In the same way, you can use these technologies to estimate the career path of your employees to prepare them for career advancement.
Estimating Employee Morale
The HR industry is increasingly leveraging AI and ML as they are clever at identifying performance patterns over time. These innovations come with face-recognition technologies that are capable of recognizing gender and measuring employees’ emotional traits on a scale from very sad to excited. With the data gathered by these technologies, companies can develop a closer bond with their employees by using the derived insights to empower employees so that they can identify their true potential.
Streamlines Hiring Process
AI and ML have enhanced every stage of the hiring process by equipping HR teams with personalized research tools to find the best talent in the industry. An applicant tracking software (ATS) can relieve the trouble of an HR recruiter who has to go through numerous resumes, thereby reducing blunders and ambiguities during recruitment.
The software can analyze countless resumes based on keywords, location, skills, and experience. Just tell the system about a position you need to fill, and it will instantly recommend the right candidate. Natural language processing, an inbuilt tool available in the application, drives predictive language analysis to speed up recruitment by enabling HR teams to shortlist candidates faster and with fewer slip-ups.
Simplifies Payroll Processing
HR bots can also take care of payroll processing and employee expenses management. You may not need to spend time filling out forms for documenting business T&E expenses. Just notify the bot, and it will get your bills approved by your manager.
Better Prediction Models
AI and ML have the potential to know your business better – whether it is predicting your future ROI, employee engagement level, problems related to the completion of projects, and other issues that would generally take years to come into sight.
AI and ML are two crucial technology trends that need to be implemented for enhancing data-driven decision making and effective HR management. To overcome the hurdles, HR teams should work in combination with advanced technologies to understand the power of robotics in employee management.
| 2020-02-22T00:00:00 |
2020/02/22
|
https://www.sutisoft.com/blog/the-impact-of-ai-and-machine-learning-on-workforce-management/
|
[
{
"date": "2020/02/21",
"position": 42,
"query": "machine learning workforce"
},
{
"date": "2020/02/21",
"position": 45,
"query": "machine learning workforce"
},
{
"date": "2020/02/21",
"position": 43,
"query": "machine learning workforce"
},
{
"date": "2020/02/21",
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},
{
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},
{
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Talent and workforce effects in the age of AI - Deloitte
|
Talent and workforce effects in the age of AI
|
https://www.deloitte.com
|
[
"Research Manager",
"Susanne Hupfer"
] |
Global AI adoption and investment are soaring. By one account, 37 percent of organizations have deployed AI solutions—up 270 percent from four ...
|
Introduction
Over the past few years, artificial intelligence has matured into a collection of powerful technologies that are delivering competitive advantage to businesses across industries. Global AI adoption and investment are soaring. By one account, 37 percent of organizations have deployed AI solutions—up 270 percent from four years ago.1 Analysts forecast global AI spending will more than double over the next three years, topping US$79 billion by 2022.2
Companies and countries around the globe increasingly view development of strong AI capabilities as imperative to staying competitive. Deloitte’s State of AI in the Enterprise, 2nd Edition offers a global perspective of AI early adopters, based on surveying 1,900 IT and business executives from seven countries and a variety of industries.3 These adopters are increasing their spending on AI technologies and realizing positive returns. Almost two-thirds (65 percent) report that AI technologies are enabling their organizations to move ahead of the competition. Sixty-three percent of the leaders surveyed already view AI as “very” or “critically” important to their business success, and that number is expected to grow to 81 percent within two years.
These leaders see AI rapidly transforming their businesses and industries. Fifty-seven percent predict that AI will “substantially transform” their company within the next three years; two-thirds believe that their industry’s transformation will happen within five years. As AI drives these transformations, it is changing how work gets done in organizations by making operations more efficient, supporting better decision-making, and freeing up workers from certain tasks. The nature of job roles, and the skills that are most needed, are evolving.
Indeed, the effect AI will ultimately have on jobs is uncertain: Are we staring at a dim future in which AI-driven automation has made most jobs obsolete, or is AI ushering in a new age characterized by humans working in collaboration with the technologies—augmented by AI capabilities rather than displaced by them?4 Early indicators support the optimistic view: While AI adopters express concern about automation as an ethical risk, they emphatically believe that human workers and AI will augment each other, changing the nature of work for the better.
The changing nature of work
As AI adoption advances, the way organizations do their work is evolving. Seventy-one percent of adopters report that AI technologies have already changed their company’s job roles and necessary skills, and 82 percent believe AI will lead to moderate or substantial changes to job roles and skills over the next three years.
For AI adopters, improving internal business operations is a benefit on par with enhancing products and services (figure 1). TiVo, for example, streamlines IT operations by using a machine learning5 platform to automatically detect, classify, aggregate, and route IT incidents.6 The AI-aided process has reduced actionable events from about 2,500 to 150 daily, enabling the professionals in TiVo’s network operations center to more easily manage highly complex operations, 24/7.
The third AI benefit—making better decisions—also has implications for the nature of work. For example, researchers from MIT have developed a machine learning model designed to help ER physicians determine the optimal time to switch patients suffering from sepsis from one treatment protocol to another—often a challenging decision for clinicians.7 Trained on historic health data from sepsis patients, the model predicts whether a patient will need vasopressor medications within the next few hours. In a clinical setting, the model could be integrated into a bedside monitor, alerting clinicians ahead of time when a treatment change may be warranted—an example of human experts and AI achieving better decisions together.
Another top benefit of AI involves automating tasks to free up workers to be more creative. Salesforce's Einstein Voice Assistant—a voice-based AI assistant for interacting with Salesforce CRM software—illustrates this benefit: Sales reps and other field workers speak conversationally to the assistant, which transcribes notes, automatically associates them with relevant accounts and contacts, and makes recommendations for follow-up tasks.8 Workers are freed from mundane data entry tasks and can instead concentrate their efforts on their customer interactions.
| 2020-03-03T00:00:00 |
https://www.deloitte.com/us/en/insights/topics/emerging-technologies/ai-adoption-in-the-workforce.html
|
[
{
"date": "2020/03/03",
"position": 39,
"query": "workplace AI adoption"
},
{
"date": "2020/03/03",
"position": 41,
"query": "workplace AI adoption"
},
{
"date": "2020/03/03",
"position": 40,
"query": "workplace AI adoption"
},
{
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},
{
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"query": "workplace AI adoption"
},
{
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},
{
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"query": "workplace AI adoption"
},
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"query": "workplace AI adoption"
},
{
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"query": "workplace AI adoption"
},
{
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"query": "workplace AI adoption"
},
{
"date": "2020/03/03",
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"query": "workplace AI adoption"
},
{
"date": "2020/03/03",
"position": 41,
"query": "workplace AI adoption"
},
{
"date": "2020/03/03",
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},
{
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},
{
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] |
|
Transforming healthcare with AI: The impact on the workforce and ...
|
Transforming healthcare with AI: The impact on the workforce and organizations
|
https://www.mckinsey.com
|
[
"Angela Spatharou",
"Solveigh Hieronimus",
"Jonathan Jenkins"
] |
AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the day-to-day life of ...
|
Healthcare is one of the major success stories of our times. Medical science has improved rapidly, raising life expectancy around the world, but as longevity increases, healthcare systems face growing demand for their services, rising costs and a workforce that is struggling to meet the needs of its patients.
Demand is driven by a combination of unstoppable forces: population aging, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation being but a few. Of these, the implications from an aging population stand out. By 2050, one in four people in Europe and North America will be over the age of 65—this means the health systems will have to deal with more patients with complex needs. Managing such patients is expensive and requires systems to shift from an episodic care-based philosophy to one that is much more proactive and focused on long-term care management.
Healthcare spending is simply not keeping up. Without major structural and transformational change, healthcare systems will struggle to remain sustainable. Health systems also need a larger workforce, but although the global economy could create 40 million new health-sector jobs by 2030, there is still a projected shortfall of 9.9 million physicians, nurses and midwives globally over the same period, according to the World Health Organization. We need not only to attract, train and retain more healthcare professionals, but we also need to ensure their time is used where it adds most value—caring for patients.
Building on automation, artificial intelligence (AI) has the potential to revolutionize healthcare and help address some of the challenges set out above. There are several definitions of AI, but this report draws from a concise and helpful definition used by the European Parliament, “AI is the capability of a computer program to perform tasks or reasoning processes that we usually associate with intelligence in a human being.” AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the day-to-day life of healthcare practitioners, letting them spend more time looking after patients and in so doing, raise staff morale and improve retention. It can even get life-saving treatments to market faster. At the same time, questions have been raised about the impact AI could have on patients, practitioners, and health systems, and about its potential risks; there are ethical debates around how AI and the data that underpins it should be used.
This EIT Health and McKinsey & Company report aims to contribute to the debate surrounding AI in healthcare, specifically looking at how practitioners and organizations will be affected. It aims to cast light on the priorities and trade-offs for different parts of the healthcare system in Europe and beyond. The report draws on proprietary research and analyses undertaken by EIT Health and McKinsey & Company. This includes work by the McKinsey Global Institute (MGI) on the future of work in the era of automation and AI, analyzing the impact on healthcare practitioners in Europe; a series of one-to-one interviews with 62 healthcare and other leaders with experience in AI and digital health, and an online survey of 175 healthcare professionals, healthcare investors, and AI startup founders and other executives. AI in healthcare being a fast-moving field, the report provides a unique vantage point from the frontline of healthcare delivery and innovation today and the latest view from a wide array of stakeholders on AI’s potential, the real state of play today, and what is holding us back.
Last, to highlight where AI is already having an impact in healthcare, the report also looks at detailed examples of existing AI solutions in six core areas where AI has a direct impact on the patient and three areas of the healthcare value chain that could benefit from further scaling of AI (Exhibit 1).
In doing so, the report provides a unique contribution to the debate on the impact of AI in healthcare in four ways: 1) decision makers’ view of the state-of-play in this fast-moving field, where developments from just 12 months ago are considered “old news”; 2) a robust new methodology to evaluate the impact of automation and AI on specific skills and activities in healthcare in Europe; 3) a substantial review of use cases that illustrate the potential that AI is already on track to deliver; and 4) a unique view from the frontline, hearing from healthcare professionals, investors and startup executives on where the real potential, opportunities and barriers lie.
The report does not attempt to cover all facets of this complex issue, in particular the ethics of AI or managing AI-related risks, but does reflect the efforts on this important topic led by EIT Health and other EU institutions. Equally, while it acknowledges the potential disruptive impact of personalization on both healthcare delivery and healthcare innovation in the future (e.g., in R&D), the report focuses primarily on the impact of AI on healthcare professionals and organizations, based on the use cases available today.
Last, AI is in its infancy and its long-term implications are uncertain. Future applications of AI in healthcare delivery, in the approach to innovation and in how each of us thinks about our health, may be transformative. We can imagine a future in which population-level data from wearables and implants change our understanding of human biology and of how medicines work, enabling personalized and real-time treatment for all. This report focuses on what is real today and what will enable innovation and adoption tomorrow, rather than exploring the long-term future of personalized medicine. Faced with the uncertainty of the eventual scope of application of emerging technologies, some short-term opportunities are clear, as are steps that will enable health providers and systems to bring benefits from innovation in AI to the populations they serve more rapidly.
AI in healthcare today
More data, better data, more connected data
What do we mean by AI in healthcare? In this report we include applications that affect care delivery, including both how existing tasks are performed and how they are disrupted by changing healthcare needs or the processes required to address them. We also include applications that enhance and improve healthcare delivery, from day-to-day operational improvement in healthcare organizations to population-health management and the world of healthcare innovation. It’s a broad definition that covers natural language processing (NLP), image analysis, and predictive analytics based on machine learning. As such, it illustrates a spectrum of AI solutions, where encoding clinical guidelines or existing clinical protocols through a rules-based system often provides a starting point, which then can be augmented by models that learn from data.
AI is now top-of-mind for healthcare decision makers, governments, investors and innovators, and the European Union itself. An increasing number of governments have set out aspirations for AI in healthcare, in countries as diverse as Finland, Germany, the United Kingdom, Israel, China, and the United States and many are investing heavily in AI-related research. The private sector continues to play a significant role, with venture capital (VC) funding for the top 50 firms in healthcare-related AI reaching $8.5 billion, and big tech firms, startups, pharmaceutical and medical-devices firms and health insurers, all engaging with the nascent AI healthcare ecosystem.
Geographically, the dynamics of AI growth are shifting. The United States still dominates the list of firms with highest VC funding in healthcare AI to date, and has the most completed AI-related healthcare research studies and trials. But the fastest growth is emerging in Asia, especially China, where leading domestic conglomerates and tech players have consumer-focused healthcare AI offerings and Ping An’s Good Doctor, the leading online health-management platform already lists more than 300 million users. Europe, meanwhile, benefits from the vast troves of health data collected in national health systems and has significant strengths in terms of the number of research studies, established clusters of innovation and pan-European collaborations, a pan-European approach to core aspects of AI (e.g., ethics, privacy, “trustworthy AI”) and an emerging strategy on how to ensure the “EU way” for AI helps deliver the advantages for AI to its population. Yet, at the same time, valuable data sets are not linked, with critical data-governance, access, and security issues still needing to be clarified, delaying further adoption. European investment and research in AI are strong when grouped together but fragmented at the country or regional level. Overall, there is a significant opportunity for EU health systems, but AI’s full potential remains to be explored and the impact on the ground remains limited. A surprising 44 percent of the healthcare professionals we surveyed—and these were professionals chosen based on their engagement with healthcare innovation—had never been involved in the development or deployment of an AI solution in their organization.
Growing number of use cases
While there are widespread questions on what is real in AI in healthcare today, this report looked at 23 applications in use today and provides case studies of 14 applications already in use. These illustrate the full range of areas where AI can have impact: from apps that help patients manage their care themselves, to online symptom checkers and e-triage AI tools, to virtual agents that can carry out tasks in hospitals, to a bionic pancreas to help patients with diabetes. Some help improve healthcare operations by optimizing scheduling or bed management, others improve population health by predicting the risk of hospital admission or helping detect specific cancers early enabling intervention that can lead to better survival rates; and others even help optimize healthcare R&D and pharmacovigilance. The scale of many solutions remains small, but their increasing adoption at the health-system level indicates the pace of change is accelerating. In most cases, the question is less whether AI can have impact, and more how to increase the potential for impact and, crucially, how to do so while improving the user experience and increasing user adoption.
Three phases of scaling AI in healthcare
We are in the very early days of our understanding of AI and its full potential in healthcare, in particular with regards to the impact of AI on personalization. Nevertheless, interviewees and survey respondents conclude that over time we could expect to see three phases of scaling AI in healthcare, looking at solutions already available and the pipeline of ideas.
First, solutions are likely to address the low-hanging fruit of routine, repetitive and largely administrative tasks, which absorb significant time of doctors and nurses, optimizing healthcare operations and increasing adoption. In this first phase, we would also include AI applications based on imaging, which are already in use in specialties such as radiology, pathology, and ophthalmology.
In the second phase, we expect more AI solutions that support the shift from hospital-based to home-based care, such as remote monitoring, AI-powered alerting systems, or virtual assistants, as patients take increasing ownership of their care. This phase could also include a broader use of NLP solutions in the hospital and home setting, and more use of AI in a broader number of specialties, such as oncology, cardiology, or neurology, where advances are already being made. This will require AI to be embedded more extensively in clinical workflows, through the intensive engagement of professional bodies and providers. It will also require well designed and integrated solutions to use existing technologies effectively in new contexts. This scaling up of AI deployment would be fuelled by a combination of technological advancements (e.g., in deep learning, NLP, connectivity etc.) and cultural change and capability building within organizations.
In the third phase, we would expect to see more AI solutions in clinical practice based on evidence from clinical trials, with increasing focus on improved and scaled clinical decision-support (CDS) tools in a sector that has learned lessons from earlier attempts to introduce such tools into clinical practice and has adapted its mind-set, culture and skills. Ultimately respondents would expect to see AI as an integral part of the healthcare value chain, from how we learn, to how we investigate and deliver care, to how we improve the health of populations. Important preconditions for AI to deliver its full potential in European healthcare will be the integration of broader data sets across organizations, strong governance to continuously improve data quality, and greater confidence from organizations, practitioners and patients in both the AI solutions and the ability to manage the related risks.
How will AI change the healthcare workforce?
The MGI has studied how automation and AI are likely to affect the future of work. It concludes that automation will affect most jobs across sectors, but the degree varies significantly, and healthcare is one of the sectors with the lowest overall potential for automation—only 35 percent of time spent is potentially automatable and this varies by type of occupation. The potential for automation is different to the likelihood of adoption.
The analysis uses a midpoint scenario, which estimates that 15 percent of current work hours in healthcare are expected to be automated. Exhibit 2 shows the share of hours currently worked that could be freed up by automation by 2030 for a wide range of healthcare occupations in selected European countries. This does not reflect the potential for further disruption through other factors, such as personalization, that may revolutionize healthcare by focusing on a “segment of one.”
How will automation and AI affect the number of jobs in healthcare? The reality is that the European healthcare sector faces a significant workforce gap that is only expected to widen. The World Health Organization estimates overall demand for healthcare workers to rise to 18.2 million across Europe by 2030 and, as an example, states that the current supply of 8.6 million nurses, midwives, and healthcare assistants across Europe will not meet current or projected future need. The MGI analysis of the demand for specific types of healthcare activities suggests significant increases in the need for specific professionals, such as licensed practical and vocational nurses, home health aides, and others, who are core to the day-to-day delivery of care to European citizens. It highlights that automation could, in fact, alleviate workforce shortages in healthcare, as demand for occupations is set to increase. For example, a 39 percent increase in all nursing occupations is expected by 2030, even allowing for the fact that approximately 10 percent of nursing activities could be freed up by automation.
The impact on the workforce will be much more than jobs lost or gained—the work itself will change. At the heart of any change is the opportunity to refocus on and improve patient care. AI can help remove or minimize time spent on routine, administrative tasks, which can take up to 70 percent of a healthcare practitioner’s time. A recurring theme in interviews was that this type of AI role would not just be uncontroversial but would top of most people’s wish list and would speed up adoption. AI can go further. It can augment a range of clinical activities and help healthcare practitioners access information that can lead to better patient outcomes and higher quality of care. It can improve the speed and accuracy in use of diagnostics, give practitioners faster and easier access to more knowledge, and enable remote monitoring and patient empowerment through self-care. This will all require bringing new activities and skills into the sector, and it will change healthcare education—shifting the focus away from memorizing facts and moving to innovation, entrepreneurship, continuous learning, and multidisciplinary working. The biggest leap of all will be the need to embed digital and AI skills within healthcare organizations—not only for physicians to change the nature of consultations, but for all frontline staff to integrate AI into their workflow. This is a significant change in organizational culture and capabilities, and one that will necessitate parallel action from practitioners, organizations and systems all working together.
The final effect on the workforce will be the introduction of new professionals. Multiple roles will emerge at the intersection of medical and data-science expertise. For example, medical leaders will have to shape clinically meaningful and explainable AI that contains the insights and information to support decisions and deepen healthcare professionals’ understanding of their patients. Clinical engagement will also be required in product leadership, in order to determine the contribution of AI-based decision-support systems within broader clinical protocols. Designers specializing in human-machine interactions on clinical decision making will help create new workflows that integrate AI. Data architects will be critical in defining how to record, store and structure clinical data so that algorithms can deliver insights, while leaders in data governance and data ethics will also play vital roles. In other data-rich areas, such as genomics, new professionals would include ‘hybrid’ roles, such as clinical bioinformaticians, specialists in genomic medicine, and genomic counsellors. Institutions will have to develop teams with expertise in partnering with, procuring, and implementing AI products that have been developed or pioneered by other institutions. Orchestrating the introduction of new specializations coming from data science and engineering within healthcare delivery will become a critical skill in itself. There will be an urgent need for health systems to attract and retain such scarce and valuable talent, for example, by developing flexible and exciting career paths and clear routes to leadership roles.
What needs to change to encourage the introduction and scaling of AI in healthcare?
The strides made in the field of AI in healthcare have been momentous. Moving to a world in which AI can deliver significant, consistent, and global improvements in care will be more challenging.
Of course, AI is not a panacea for healthcare systems, and it comes with strings attached. The analyses in this report and the latest views from stakeholders and frontline staff reveal a set of themes that all players in the healthcare ecosystem will need to address:
Working together to deliver quality AI in healthcare. Quality came up in our interviews time and again, especially issues around the poor choice of use cases, AI design and ease of use, the quality and performance of algorithms, and the robustness and completeness of underlying data. The lack of multidisciplinary development and early involvement of healthcare staff, and limited iteration by joint AI and healthcare teams were cited as major barriers to addressing quality issues early on and adopting solutions at scale. The survey revealed this is driven by both sides: only 14 percent of startup executives felt that the input of healthcare professionals was critical in the early design phase; while the healthcare professionals saw the private sector’s role in areas such as aggregating or analyzing data, providing a secure space for data lakes, or helping upskill healthcare staff as minimal or nonexistent.
One problem AI solutions face is building the clinical evidence of quality and effectiveness. While startups are interested in scaling solutions fast, healthcare practitioners must have proof that any new idea will “do no harm” before it comes anywhere near a patient. Practitioners also want to understand how it works, where the underlying data come from and what biases might be embedded in the algorithms, so are interested in going past the concept of AI as a “black box” to understand what underpins it. Transparency and collaboration between innovators and practitioners will be key in scaling AI in European healthcare.
User-centric design is another essential component of a quality product. Design should have the end user at its heart. This means AI should fit seamlessly with the workflow of decision makers and by being used, it will be improved. Many interviewees agreed that if AI design delivers value to end users, those users are more likely to pay attention to the quality of data they contribute, thereby improving the AI and creating a virtuous circle. Finally, AI research needs to heavily emphasize explainable, causal, and ethical AI, which could be a key driver of adoption. Rethinking education and skills. We have already touched on the importance of digital skills—these are not part of most practitioners’ arsenal today. AI in healthcare will require leaders well-versed in both biomedical and data science. There have been recent moves to train students in the science where medicine, biology, and informatics meet through joint degrees, though this is less prevalent in Europe. More broadly, skills such as basic digital literacy, the fundamentals of genomics, AI, and machine learning need to become mainstream for all practitioners, supplemented by critical-thinking skills and the development of a continuous-learning mind-set. Alongside upgrading clinical training, healthcare systems need to think about the existing workforce and provide ongoing learning, while practitioners need the time and incentive to continue learning. Strengthening data quality, governance, security and interoperability. Both interviewees and survey respondents emphasized that data access, quality, and availability were potential roadblocks. The data challenge breaks down into digitizing health to generate the data, collecting the data, and setting up the governance around data management. MGI analyses show that healthcare is among the least digitized sectors in Europe, lagging behind in digital business processes, digital spend per worker, digital capital deepening, and the digitization of work and processes. It is critical to get the basic digitization of systems and data in place before embarking on AI deployments—not least because the frustrations staff have with basic digitization could spill over to the wider introduction of AI.
In addition, as more healthcare is delivered using new digital technologies, public concerns about how healthcare data are used have grown. Healthcare organizations should have robust and compliant data-sharing policies that support the improvements in care that AI offers while providing the right safeguards in a cost-efficient way. Physicians we interviewed emphasized that, given the volume of data required for AI, a poorly thought out process of anonymization could be a major cost, making diagnostic algorithms prohibitively expensive.
Interviewees also emphasized, however, that both healthcare as a sector and Europe as a region have significant advantages. First, both healthcare organizations and health systems are used to dealing with sensitive data through well-structured data governance and risk-management processes. In some cases, healthcare could lead the way for other sectors seeking to put such measures in place. Secondly, Europe benefits from national health systems with extensive data sets, often shared within integrated care systems, offering a set of systems and processes to build on that could also serve as examples to other regions.
The final data challenge is getting data sets to talk to each other. Policymakers, funding bodies and nonprofit organizations need to support efforts to sufficiently anonymize and link data and, where sensible, to build databases that can be accessed by stakeholders with the appropriate safeguards. In order to make the most of the rich data that is available, healthcare systems need an interconnected data infrastructure. This is an area where Europe, as mentioned, could have a significant advantage, in terms of its extensive national data sets and its networks of innovations clusters or hubs and pan-European collaborations with academia and industry, providing a prototype for the creation of centers of excellence for AI in healthcare. Managing change. Managing change while introducing AI is no different to managing change in complex institutions more broadly, but for healthcare, clinical leadership is key, as is being open to identifying the right use cases that support rather than antagonize practitioners and truly augment rather than substitute their ability to deliver the best possible care to their patients. This could include prioritizing solutions that focus on reducing the time people spend on routine administrative tasks, rather than those that seek to act as virtual assistants who interact directly with patients, or CDS tools that facilitate activities physicians see as core to their professional role, i.e., the clinical diagnosis.
Healthcare providers also need to be transparent about the benefits and risks of AI and work with staff to harness the collective energy of their teams and capitalize on the opportunities AI can bring. It may not be a rapid process, but it soon becomes increasingly rewarding for practitioners and is an important part of the overall adoption process. Investing in new talent and creating new roles. Healthcare organizations need to consider how they will develop and recruit the new roles that will be critical to the successful introduction and adoption of AI, such as data scientists or data engineers. Demand for such skills is heating up across industries and the competition for talent will be fierce, but many young data professionals find a true vocation in healthcare and its mission and are excited about the potential of digital health and AI. Developing flexible, agile models to attract and retain such talent will be a key part of these organizations’ people strategy. Working at scale. The lessons from public- and private-sector actors aiming to develop AI in healthcare to date suggest that scale matters—largely due to the resources needed to develop robust AI solutions or make them cost-efficient. Not every hospital will be able to afford to attract new AI talent, or have access to enough data to make algorithms meaningful. Smaller organizations can benefit from working in innovation clusters that bring together AI, digital health, biomedical research, translational research or other relevant fields. Larger organizations can develop into centres of excellence that pave the way for regional and public-private collaborations to scale AI in European healthcare. Regulation, policymaking and liability, and managing risk. Responsibility for AI solutions—both clinical and technical—is split today between healthcare organizations and their staff. Interviewees emphasized the importance of clarifying whether AI will be regulated as a product or as a tool that supports decision making, and of introducing a consistent regulatory approach for AI similar to that provided by the European Medicines Agency (EMA) on medicines or by national authorities on medical devices. Another issue to be clarified across Europe is the extent to which patients’ access to some AI tools needs to be regulated or restricted to prescription. The issue of liability and risk management is a particular challenge. Patient safety is paramount, but healthcare providers also have to think about the professional accountability of their clinicians, as well the protection of their organizations from reputational, legal or financial risk. Healthcare lawyers interviewed in this report were clear that accountability ultimately rests with the clinician under current laws. Innovators are also proactively addressing related risks. Many are putting new processes in place and ensuring a “compliance by design” approach is at the core of product development. Funding. The reimbursement of medicines and medical devices across Europe is complicated and is even less clear when it comes to AI solutions. The responsibility for decisions on the reimbursement of a medicine or device rests with national and local payor organizations depending on the country, and this decision usually covers what will be reimbursed and at what price. Clear criteria for the potential reimbursement of AI applications will be crucial for its adoption at scale, alongside creative funding models that ensure the benefits are shared across organizations.
What this could mean for healthcare organizations
European healthcare providers need to assess what their distinctive role or contribution can be in introducing or scaling AI in healthcare. They need to take stock of their capabilities, level of digitization, availability and quality of data, resources and skills and then define their level of ambition for AI as it fits with their strategic goals. They should also define the enablers they need to put in place. These could include creating an AI ecosystem through partnerships to codevelop the right solutions for their population; codeveloping a compelling narrative on AI with patients and practitioners; defining and developing the right use cases jointly with end users; defining and addressing skill gaps in digital literacy for their staff; refining their value proposition for AI talent; addressing data-quality, access, governance, and interoperability issues; and shaping a culture of entrepreneurship. All these themes were echoed by the healthcare professionals in the survey, who listed the top three things healthcare organizations could do, as: bringing together multidisciplinary teams with the right skills, improving the quality and robustness of data and identifying the right use cases.
What this could mean for health systems
European health systems can play a more fundamental role in catalyzing the introduction and scaleup of AI. Key actions they could take include:
Develop a regional or national AI strategy for healthcare, defining a medium- and longer-term vision and goals, specific initiatives, resources and performance indicators. Define use cases to support through targeted funding and incentives to enable scaling of AI solutions across the system; ensure these deliver against both clinical and operational outcomes.
Set standards for digitization, data quality and completeness, data access, governance, risk management, security and sharing, and system interoperability; incentivize adherence to standards through a combination of performance and financial incentives.
Redesign workforce planning and clinical-education processes to address the needs of both future healthcare and AI-focused professionals; and invest upfront in upskilling frontline staff and designing lifelong-learning programs through continuing professional development and degrees or diplomas for healthcare professionals.
Provide incentives and guidance for healthcare organizations to collaborate in centers of excellence/clusters of innovation at the regional or national level.
Address AI regulation, liability and funding issues, creating the right environment for appropriate, safe and effective AI solutions to be adopted but minimizing the risk to practitioners.
Ensure this is reflected in funding and reimbursement mechanisms for innovation in healthcare—the number one priority for survey respondents from health systems, alongside simplifying data-governance and data-sharing processes.
What this could mean for Europe
Our early analyses of levels of VC investment and AI-related clinical trials, as well as the number of companies and M&A deals in digital health and AI, show this is a fast-moving market where Europe, as a group of countries, plays a growing role internationally alongside the United States and China. The scale needed to effectively roll out AI in healthcare may place a toll on smaller EU Member States but could be easily reached through collaborations across Europe. Interviewees and survey respondents were clear on the potential impact of the European Union in helping deliver the promise of AI, faster and at a greater scale for Europe’s population. They highlighted the following specific strands of work that could be considered:
Consolidating funding against strategic AI priorities. Defining a few concrete priorities for AI in European healthcare and consolidating funding to support them strategically could provide a much-needed stimulus to fast-track promising developments in AI for healthcare.
Defining a few concrete priorities for AI in European healthcare and consolidating funding to support them strategically could provide a much-needed stimulus to fast-track promising developments in AI for healthcare. Creating a level playing field across Europe. Common standards on data, regulation, access, privacy, or interoperability, and shared requirements on data exchange, would enable innovators to scale AI solutions cost-effectively, while focusing their energies on entrepreneurship. It would also enable patients, practitioners, and health systems to develop the same confidence in new AI solutions that they have now in new medicines and medical devices that have undergone European approval.
Common standards on data, regulation, access, privacy, or interoperability, and shared requirements on data exchange, would enable innovators to scale AI solutions cost-effectively, while focusing their energies on entrepreneurship. It would also enable patients, practitioners, and health systems to develop the same confidence in new AI solutions that they have now in new medicines and medical devices that have undergone European approval. Clarifying key aspects of regulation around product approval, accountability, governance and litigation. The European Union can help remove barriers to adopting AI at the national and local level, providing clarity on approval processes across Europe, potentially creating regulatory centers of excellence for AI regulation, and setting expectations on accountability and liability.
The European Union can help remove barriers to adopting AI at the national and local level, providing clarity on approval processes across Europe, potentially creating regulatory centers of excellence for AI regulation, and setting expectations on accountability and liability. Encouraging and supporting the creation of centers of excellence for AI in healthcare. This can help consolidate scarce AI talent in high-profile and agile networks that can move quickly from design to implementation and spearhead the introduction of new capabilities in national health systems. These centers of excellence would also lead the way in adopting and implementing technologies and approaches developed elsewhere. Indeed, their expertise in applying approaches to improve care will be as critical as their expertise in developing those approaches in the first place. They can also ensure that talent creation and continuous learning are prioritized and enhanced at the European level.
This can help consolidate scarce AI talent in high-profile and agile networks that can move quickly from design to implementation and spearhead the introduction of new capabilities in national health systems. These centers of excellence would also lead the way in adopting and implementing technologies and approaches developed elsewhere. Indeed, their expertise in applying approaches to improve care will be as critical as their expertise in developing those approaches in the first place. They can also ensure that talent creation and continuous learning are prioritized and enhanced at the European level. Playing an active role in AI. This will ensure that the thoughtful European approach to ethics, health data and patient confidentiality shapes the AI sector, in the same way that GDPR (General Data Protection Regulation) has for privacy protection.
Overall, this report highlights the excitement of Europe-wide stakeholders, healthcare professionals, investors, and innovators about the impact of AI on European healthcare, and about the thoughtful approach taken across Europe to ensure this delivers ethical and trustworthy AI. It also highlights that this is only the latest view across Europe and internationally—speed is of the essence if Europe is to continue playing a leading role in shaping the AI of the future to deliver its true potential to European health systems and their patients.
| 2020-03-10T00:00:00 |
https://www.mckinsey.com/industries/healthcare/our-insights/transforming-healthcare-with-ai
|
[
{
"date": "2020/03/10",
"position": 50,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/03/10",
"position": 47,
"query": "AI healthcare"
},
{
"date": "2020/03/10",
"position": 60,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/03/10",
"position": 48,
"query": "AI healthcare"
},
{
"date": "2020/03/10",
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},
{
"date": "2020/03/10",
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},
{
"date": "2020/03/10",
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},
{
"date": "2020/03/10",
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},
{
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},
{
"date": "2020/03/10",
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},
{
"date": "2020/03/10",
"position": 27,
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},
{
"date": "2020/03/10",
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},
{
"date": "2020/03/10",
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},
{
"date": "2020/03/10",
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{
"date": "2020/03/10",
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{
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{
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{
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},
{
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{
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},
{
"date": "2020/03/10",
"position": 45,
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},
{
"date": "2020/03/10",
"position": 39,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/03/10",
"position": 42,
"query": "AI healthcare"
},
{
"date": "2020/03/10",
"position": 49,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/03/10",
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"query": "artificial intelligence healthcare"
},
{
"date": "2020/03/10",
"position": 48,
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},
{
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"position": 50,
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},
{
"date": "2020/03/10",
"position": 49,
"query": "AI healthcare"
}
] |
|
Workforce Management AI - Quinyx
|
Workforce Management AI
|
https://www.quinyx.com
|
[
"Tommy Tonkins"
] |
Workforce Management AI · 1. Publically available data helps AI predictions · 2. Say hi to your new assistant · 3. Machine learning suggests ...
|
AI has already had a huge impact in the workforce management industry, helping businesses optimize across areas like performance, costs, and happiness.
Today, smart workforce management software can automatically generate schedules based on business needs and employee wishes. And it’s a trend that’s only going to increase and improve. Gartner is predicting that by 2022, 30% of Tier 1 retailers will leverage intelligent automation among their store workforce to improve business outcomes through better customer experience and associate engagement.
As AI advances it will be able to do things like suggesting (and possibly automatically performing) actions which end users simply say ‘yes’ to, learn how users behave, automate tasks, and identify trends to help with future planning.
But what will the result be?
Well, a significant amount of saved admin time, as well as always having the right people in the right place, at the right time. Something that will be of even higher significance in times where external factors force us to think differently.
We know that predicting where the industry is going to be in the future is never easy, however, here’s our take on how AI will shape workforce management over the next 5 years.
1. Publically available data helps AI predictions
Here’s a fun fact - data from Google searches in 2019 shows a direct correlation with the number of sick days taken in any month. When searches for things such as ‘colds’ and ‘influenza’ peaked, so did the number of sick days.
AI will be able to analyze this kind of data - and others like it - to give you suggested actions. For example, AI in forecasting will look at other data streams relevant to your business, such as public holidays, weather, or big events taking place in the vicinity of one of your outlets, in order to create more accurate forecasts.
2. Say hi to your new assistant
The suggested actions above will be presented to you in the form of a virtual assistant integrated within your workforce management software. Based on learnings from historical data analysis and generated forecasts, the assistant can suggest actions on how to improve the schedule, then perform multiple actions when you agree to a suggestion.
3. Machine learning suggests actions based on interaction
Machine learning is used to analyze how users (especially managers) handle different tasks. Once strong patterns are identified, they can be automated. For example, if the AI sees you’ve performed an action 20 times, it will ask you if you want to automate the action from now on.
Data will talk to you if you’re willing to listen. AI will be able to alert you to employee patterns which could for example highlight anything being a flight risk. It will also be able to give you weekly reports to help you understand things like the impact edits you made to the schedule had.
4. Automated recognition & rewards for staff
Rewards and recognition play a crucial role in employee engagement. Right now, when engagement is built into your WFM software, your employees have the ability to recognize and reward their peers with virtual badges. In Quinyx, these include Super Colleague, Problem Solver, and Game Changer.
AI will be able to automate this recognition, giving your employees automated badges for, say, being the best colleague ever. There’s also the possibility that rewards like additional compensation will be given out by AI.
5. Facial recognition, step counter & time clock integrations catch automated deviations
With this feature, AI will match images of employees punching into images you have on your employee profiles and alert the manager if it’s less than 90% likely to be the same person. Health devices and time clocks can also be connected with badges with rewards being triggered for goals like the most steps taken at work.
AI and machine learning have already played a huge role in shaping the direction the workforce management industry is traveling in.
We’re already using AI at Quinyx. We’re using it to help you make better decisions, to free up your time, to save you money, to reduce your admin, and eradicate errors. In short, we’re using it to do all the things machines excel at, giving you the freedom you need to bring the human touch to your business and create a happy workforce.
Because a happy workforce means a happy, successful, and profitable business.
Curious to find out how you can improve your employees' experience with the help of AI?
| 2020-04-29T00:00:00 |
https://www.quinyx.com/blog/workforce-management-ai
|
[
{
"date": "2020/04/29",
"position": 78,
"query": "machine learning workforce"
},
{
"date": "2020/04/29",
"position": 78,
"query": "machine learning workforce"
}
] |
|
How many jobs do robots really replace? | MIT News
|
How many jobs do robots really replace?
|
https://news.mit.edu
|
[
"Peter Dizikes"
] |
MIT professor Daron Acemoglu is co-author of a new study showing that each robot added to the workforce has the effect of replacing 3.3 jobs ...
|
This is part 1 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu.
In many parts of the U.S., robots have been replacing workers over the last few decades. But to what extent, really? Some technologists have forecast that automation will lead to a future without work, while other observers have been more skeptical about such scenarios.
Now a study co-authored by an MIT professor puts firm numbers on the trend, finding a very real impact — although one that falls well short of a robot takeover. The study also finds that in the U.S., the impact of robots varies widely by industry and region, and may play a notable role in exacerbating income inequality.
“We find fairly major negative employment effects,” MIT economist Daron Acemoglu says, although he notes that the impact of the trend can be overstated.
From 1990 to 2007, the study shows, adding one additional robot per 1,000 workers reduced the national employment-to-population ratio by about 0.2 percent, with some areas of the U.S. affected far more than others.
This means each additional robot added in manufacturing replaced about 3.3 workers nationally, on average.
That increased use of robots in the workplace also lowered wages by roughly 0.4 percent during the same time period.
“We find negative wage effects, that workers are losing in terms of real wages in more affected areas, because robots are pretty good at competing against them,” Acemoglu says.
The paper, “Robots and Jobs: Evidence from U.S. Labor Markets,” appears in advance online form in the Journal of Political Economy. The authors are Acemoglu and Pascual Restrepo PhD ’16, an assistant professor of economics at Boston University.
Displaced in Detroit
To conduct the study, Acemoglu and Restrepo used data on 19 industries, compiled by the International Federation of Robotics (IFR), a Frankfurt-based industry group that keeps detailed statistics on robot deployments worldwide. The scholars combined that with U.S.-based data on population, employment, business, and wages, from the U.S. Census Bureau, the Bureau of Economic Analysis, and the Bureau of Labor Statistics, among other sources.
The researchers also compared robot deployment in the U.S. to that of other countries, finding it lags behind that of Europe. From 1993 to 2007, U.S. firms actually did introduce almost exactly one new robot per 1,000 workers; in Europe, firms introduced 1.6 new robots per 1,000 workers.
“Even though the U.S. is a technologically very advanced economy, in terms of industrial robots’ production and usage and innovation, it’s behind many other advanced economies,” Acemoglu says.
In the U.S., four manufacturing industries account for 70 percent of robots: automakers (38 percent of robots in use), electronics (15 percent), the plastics and chemical industry (10 percent), and metals manufacturers (7 percent).
Across the U.S., the study analyzed the impact of robots in 722 commuting zones in the continental U.S. — essentially metropolitan areas — and found considerable geographic variation in how intensively robots are utilized.
Given industry trends in robot deployment, the area of the country most affected is the seat of the automobile industry. Michigan has the highest concentration of robots in the workplace, with employment in Detroit, Lansing, and Saginaw affected more than anywhere else in the country.
“Different industries have different footprints in different places in the U.S.,” Acemoglu observes. “The place where the robot issue is most apparent is Detroit. Whatever happens to automobile manufacturing has a much greater impact on the Detroit area [than elsewhere].”
In commuting zones where robots were added to the workforce, each robot replaces about 6.6 jobs locally, the researchers found. However, in a subtle twist, adding robots in manufacturing benefits people in other industries and other areas of the country — by lowering the cost of goods, among other things. These national economic benefits are the reason the researchers calculated that adding one robot replaces 3.3 jobs for the country as a whole.
The inequality issue
In conducting the study, Acemoglu and Restrepo went to considerable lengths to see if the employment trends in robot-heavy areas might have been caused by other factors, such as trade policy, but they found no complicating empirical effects.
The study does suggest, however, that robots have a direct influence on income inequality. The manufacturing jobs they replace come from parts of the workforce without many other good employment options; as a result, there is a direct connection between automation in robot-using industries and sagging incomes among blue-collar workers.
“There are major distributional implications,” Acemoglu says. When robots are added to manufacturing plants, “The burden falls on the low-skill and especially middle-skill workers. That’s really an important part of our overall research [on robots], that automation actually is a much bigger part of the technological factors that have contributed to rising inequality over the last 30 years.”
So while claims about machines wiping out human work entirely may be overstated, the research by Acemoglu and Restrepo shows that the robot effect is a very real one in manufacturing, with significant social implications.
“It certainly won’t give any support to those who think robots are going to take all of our jobs,” Acemoglu says. “But it does imply that automation is a real force to be grappled with.”
| 2020-05-04T00:00:00 |
https://news.mit.edu/2020/how-many-jobs-robots-replace-0504
|
[
{
"date": "2020/05/04",
"position": 42,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 43,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 41,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 41,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 43,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 42,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 44,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 42,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 47,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 49,
"query": "robotics job displacement"
},
{
"date": "2020/05/04",
"position": 43,
"query": "robotics job displacement"
}
] |
|
Study finds stronger links between automation and inequality
|
Study finds stronger links between automation and inequality
|
https://news.mit.edu
|
[
"Peter Dizikes"
] |
Within industries adopting automation, the study shows, the average “displacement” (or job loss) from 1947-1987 was 17 percent of jobs ...
|
This is part 3 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu.
Modern technology affects different workers in different ways. In some white-collar jobs — designer, engineer — people become more productive with sophisticated software at their side. In other cases, forms of automation, from robots to phone-answering systems, have simply replaced factory workers, receptionists, and many other kinds of employees.
Now a new study co-authored by an MIT economist suggests automation has a bigger impact on the labor market and income inequality than previous research would indicate — and identifies the year 1987 as a key inflection point in this process, the moment when jobs lost to automation stopped being replaced by an equal number of similar workplace opportunities.
“Automation is critical for understanding inequality dynamics,” says MIT economist Daron Acemoglu, co-author of a newly published paper detailing the findings.
Within industries adopting automation, the study shows, the average “displacement” (or job loss) from 1947-1987 was 17 percent of jobs, while the average “reinstatement” (new opportunities) was 19 percent. But from 1987-2016, displacement was 16 percent, while reinstatement was just 10 percent. In short, those factory positions or phone-answering jobs are not coming back.
“A lot of the new job opportunities that technology brought from the 1960s to the 1980s benefitted low-skill workers,” Acemoglu adds. “But from the 1980s, and especially in the 1990s and 2000s, there’s a double whammy for low-skill workers: They’re hurt by displacement, and the new tasks that are coming, are coming slower and benefitting high-skill workers.”
The new paper, “Unpacking Skill Bias: Automation and New Tasks,” will appear in the May issue of the American Economic Association: Papers and Proceedings. The authors are Acemoglu, who is an Institute Professor at MIT, and Pascual Restrepo PhD ’16, an assistant professor of economics at Boston University.
Low-skill workers: Moving backward
The new paper is one of several studies Acemoglu and Restrepo have conducted recently examining the effects of robots and automation in the workplace. In a just-published paper, they concluded that across the U.S. from 1993 to 2007, each new robot replaced 3.3 jobs.
In still another new paper, Acemoglu and Restrepo examined French industry from 2010 to 2015. They found that firms that quickly adopted robots became more productive and hired more workers, while their competitors fell behind and shed workers — with jobs again being reduced overall.
In the current study, Acemoglu and Restrepo construct a model of technology’s effects on the labor market, while testing the model’s strength by using empirical data from 44 relevant industries. (The study uses U.S. Census statistics on employment and wages, as well as economic data from the Bureau of Economic Analysis and the Bureau of Labor Studies, among other sources.)
The result is an alternative to the standard economic modeling in the field, which has emphasized the idea of “skill-biased” technological change — meaning that technology tends to benefit select high-skilled workers more than low-skill workers, helping the wages of high-skilled workers more, while the value of other workers stagnates. Think again of highly trained engineers who use new software to finish more projects more quickly: They become more productive and valuable, while workers lacking synergy with new technology are comparatively less valued.
However, Acemoglu and Restrepo think even this scenario, with the prosperity gap it implies, is still too benign. Where automation occurs, lower-skill workers are not just failing to make gains; they are actively pushed backward financially. Moreover, Acemoglu and Restrepo note, the standard model of skill-biased change does not fully account for this dynamic; it estimates that productivity gains and real (inflation-adjusted) wages of workers should be higher than they actually are.
More specifically, the standard model implies an estimate of about 2 percent annual growth in productivity since 1963, whereas annual productivity gains have been about 1.2 percent; it also estimates wage growth for low-skill workers of about 1 percent per year, whereas real wages for low-skill workers have actually dropped since the 1970s.
“Productivity growth has been lackluster, and real wages have fallen,” Acemoglu says. “Automation accounts for both of those.” Moreover, he adds, “Demand for skills has gone down almost exclusely in industries that have seen a lot of automation.”
Why “so-so technologies” are so, so bad
Indeed, Acemoglu says, automation is a special case within the larger set of technological changes in the workplace. As he puts it, automation “is different than garden-variety skill-biased technological change,” because it can replace jobs without adding much productivity to the economy.
Think of a self-checkout system in your supermarket or pharmacy: It reduces labor costs without making the task more efficient. The difference is the work is done by you, not paid employees. These kinds of systems are what Acemoglu and Restrepo have termed “so-so technologies,” because of the minimal value they offer.
“So-so technologies are not really doing a fantastic job, nobody’s enthusiastic about going one-by-one through their items at checkout, and nobody likes it when the airline they’re calling puts them through automated menus,” Acemoglu says. “So-so technologies are cost-saving devices for firms that just reduce their costs a little bit but don’t increase productivity by much. They create the usual displacement effect but don’t benefit other workers that much, and firms have no reason to hire more workers or pay other workers more.”
To be sure, not all automation resembles self-checkout systems, which were not around in 1987. Automation at that time consisted more of printed office records being converted into databases, or machinery being added to sectors like textiles and furniture-making. Robots became more commonly added to heavy industrial manufacturing in the 1990s. Automation is a suite of technologies, continuing today with software and AI, which are inherently worker-displacing.
“Displacement is really the center of our theory,” Acemoglu says. “And it has grimmer implications, because wage inequality is associated with disruptive changes for workers. It’s a much more Luddite explanation.”
After all, the Luddites — British textile mill workers who destroyed machinery in the 1810s — may be synonymous with technophobia, but their actions were motivated by economic concerns; they knew machines were replacing their jobs. That same displacement continues today, although, Acemoglu contends, the net negative consequences of technology on jobs is not inevitable. We could, perhaps, find more ways to produce job-enhancing technologies, rather than job-replacing innovations.
“It’s not all doom and gloom,” says Acemoglu. “There is nothing that says technology is all bad for workers. It is the choice we make about the direction to develop technology that is critical.”
| 2020-05-05T00:00:00 |
https://news.mit.edu/2020/study-inks-automation-inequality-0506
|
[
{
"date": "2020/05/05",
"position": 66,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 27,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 56,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 26,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 26,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 68,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 65,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 45,
"query": "robotics job displacement"
},
{
"date": "2020/05/05",
"position": 58,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 63,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 71,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 59,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 28,
"query": "automation job displacement"
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{
"date": "2020/05/05",
"position": 68,
"query": "automation job displacement"
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{
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"query": "automation job displacement"
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{
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"position": 64,
"query": "automation job displacement"
},
{
"date": "2020/05/05",
"position": 63,
"query": "automation job displacement"
}
] |
|
How Machine Learning Improves Efficiency and Management of ...
|
How Machine Learning Improves Efficiency and Management of Workforce
|
https://aithority.com
|
[
"Imran Ahmed"
] |
As workforce management continues to be a daunting and time-consuming task for some organizations, leveraging Machine Learning and Artificial ...
|
At a time when businesses are experiencing unimaginable disruptions, demands and priorities are continuing to shift. While maintaining functionality and financial health remains at the forefront of concerns, workforce management is an essential piece of successful business operation that must continue to be tended to despite the current climate business leaders are facing.
Companies can look to technology, such as Machine Learning and Artificial Intelligence to help streamline their workforce management processes while leveraging insights to generate stronger HR mechanisms. When pairing immense, empirical data with the power of Machine Learning and Artificial Intelligence, the possibilities are endless for better managing a global workforce—in any situation.
ADP ’s Machine Learning Models Help With Hiring
For many employers, analyzing their own workforce data isn’t enough for a predictive model. ADP DataCloud closes this gap, utilizing 30-million plus employee data points to provide a more holistic view for all employers and tapping into Machine Learning to spot historical trends. ADP DataCloud’s predictive analysis helps employers understand which candidates are most likely to stay with the company. Hiring top talent may be attractive, but not if it increases turnover.
Data can also help identify which skills are most applicable to the position for which you’re hiring. These include declared skills, such as Email Marketing, Content Strategy, and Analytics, as well as implied skills inferred from that position, including leadership skills and lead generation skills.
Further, beyond identifying strong and suitable talent, ADP DataCloud leverages Machine Learning to streamline information across industries to help inform employee contract negotiations. Oftentimes, candidate-facing solutions can be outdated and inaccurate with regard to compensation. ADP’s modeling uses anonymized and aggregated real-time data from thousands of employers to surface accurate salary and benefits data. With more accurate, contextual data, new-hire negotiations become faster and more equitable for both employer and potential employee.
Read more: COVID-19 Makes Mobile Operators, AI and Analytics as Critical as Hand Sanitizer
Identifying Which Compensation Package to Offer
Desired compensation can change from role to industry. An engineer working in the manufacturing industry may expect a different package than an engineer in the technology industry. ADP leverages real-time data to helps employers know what industry-standard packages are for unique positions, nationwide and regionally.
Data from thousands of employers help business leaders to frame what’s appropriate for their own workforce and improve existing packages where necessary. Further, a closer examination of real-time trends allows companies to improve stock options and get creative with packages/vesting options.
Hiring and Retaining Talent Without Compromise
In addition to negotiating pay at hiring, ADP’s data can further be leveraged to help understand where pay needs to be shifted. Machine learning models guided by millions of data-points help companies stay competitive across roles, experience levels, and industries. While strong compensation packages are attractive to candidates, demonstrating renegotiation can appeal to internal staff. Optimizing compensation packages routinely can help entice longevity and strengthen retention.
Machine Learning feeds AI recommendations, which help large companies act fast based on algorithmic recommendations. Existing dashboards within ADP’s DataCloud solution allow decisionmakers access to accurate data in real-time in a digestible, understandable format. Data mashups allow cross-referencing of data; comparing sales trends to per-employee benefits spending, for instance. These tools directly feed into tough decisions that need to be made, not only aiding efficiency but also improving quality as decisions are informed and backed by strong data.
For example, identifying behaviors among existing employees related to absence and tardiness can help HR departments prepare to hire for a role where an employee may be exiting.
Read more: How to Use Video Ads to Promote Your Businesses on Facebook
Customized Data Points Lead to Predictive Analysis at Scale
While data aggregation can sometimes be overcomplicated as not all businesses operate on the same level and not every decision can be determined by empirical data, simpler tasks like tracking overall employee tardiness can be customized at a company level. This granular customization allows businesses to make decisions more contextual to their workforce, and personalized data can be compared against the larger dataset to identify whether a company and staff are over or underachieving in various areas.
Workforce management remains and will continue to remain an area of business that requires a level of attention. Neglecting one’s workforce to prioritize larger business needs can jeopardize the future of the company. As workforce management continues to be a daunting and time-consuming task for some organizations, leveraging Machine Learning and Artificial Intelligence can prove helpful in streamlining and informing human resource operations.
ADP’s DataCloud solution taps into these advanced technology tools help companies identify the right candidates and make them the best competitive offer possible, while simultaneously providing customizable solutions to help companies make the right decision for their needs.
The fact of the matter is, the ability to measure industry-wide data and employment trends—alongside company sales or production goals—ensures companies make the right decisions early and often; and ultimately remain competitive in the hiring and retention landscape.
Read more: The Privacy Pendulum: How Millennial and Gen Z Perceptions of Search and Privacy are Changing
| 2020-05-15T00:00:00 |
2020/05/15
|
https://aithority.com/guest-authors/how-machine-learning-improves-efficiency-and-management-of-workforce/
|
[
{
"date": "2020/05/15",
"position": 66,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 62,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 62,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 73,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 73,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 73,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 63,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 62,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 67,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 63,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 61,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 68,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 66,
"query": "machine learning workforce"
},
{
"date": "2020/05/15",
"position": 67,
"query": "machine learning workforce"
}
] |
Economics Is All About Disruption - CSIS
|
Economics Is All About Disruption
|
https://www.csis.org
|
[
"Matthew P. Goodman"
] |
Technology: Whether through a “Zoom effect” reshaping the workplace or in response to demands for new health-surveillance methods, technology— ...
|
Since the first caveman sharpened a stone into a tool, economics has involved disruption. New technologies create innovative ways of doing business and displace old ones. The Austrian economist Joseph Schumpeter coined a phrase for this: “creative destruction.” The term encompasses both the benign and harmful dimensions of economic disruption: when the forest burns, many trees are destroyed, but new growth is enabled. The key to progress is ensuring that there are more winners than losers from disruptive change and that the latter benefit from the broader gains in prosperity.
The Covid-19 pandemic feels more destructive than creative at the moment. That is certainly true when it comes to the toll in human lives: over 5 million cases and 350,000 deaths worldwide as of this writing. And the economic devastation is also profound, whether measured by the expected sharp drop in global gross domestic product (GDP) in 2020 or the nearly 40 million Americans who have lost their jobs since mid-March.
The longer-term implications of the pandemic for society, international relations, and the global economy are less certain. Change is likely in all these areas, but past crises suggest we are exaggerating certain effects and underestimating others. The changes in air travel following 9/11 turned out to be inconveniences rather than fundamental breaks; on the other hand, it is only in hindsight that we can see how much the terrorist attacks prompted a 20-year shift in America’s engagement in the world.
In this crisis as well, both benign and harmful forces will be at play, and it is not clear which will dominate where. In some cases, the pandemic will accelerate harmful trends already underway; in others, it will create new opportunities not imagined before the crisis. How individual countries respond to this moment may very well determine their place in the post-Covid-19 global economy.
Government policy choices tilt the balance between benefits and costs. Intervene too much, and the disruption could be amplified or new problems created; not enough, and the pain will be prolonged and opportunities missed. For U.S. international economic policymakers, getting the balance right is likely to be especially important in a few key areas:
Debt: Governments around the world have been forced to open the fiscal floodgates to compensate for lost economic activity and employment. The consequence has been a spike in already-rising debt: in the advanced world alone, the Organisation for Economic Co-operation and Development (OECD) projects that government financial liabilities will rise to an average of 137 percent of GDP in 2020 from 109 percent last year. The burden is likely to weigh more heavily on lower-income countries, which have yet to feel the full impact of the health crisis and have less capacity to finance their debt. How, and how fast, to stem this tide of red ink—especially when demands for government spending are only likely to grow—will bedevil policymakers for years to come.
Governments around the world have been forced to open the fiscal floodgates to compensate for lost economic activity and employment. The consequence has been a spike in already-rising debt: in the advanced world alone, the Organisation for Economic Co-operation and Development (OECD) projects that government financial liabilities will rise to an average of 137 percent of GDP in 2020 from 109 percent last year. The burden is likely to weigh more heavily on lower-income countries, which have yet to feel the full impact of the health crisis and have less capacity to finance their debt. How, and how fast, to stem this tide of red ink—especially when demands for government spending are only likely to grow—will bedevil policymakers for years to come.
Trade: Long before the pandemic, growth of world trade had fallen behind that of global GDP, and the trend toward globalization of supply chains had slowed or reversed. Calls to “re-shore” medical and pharmaceutical production in the wake of the pandemic could accelerate these trends. The trick for policymakers will be to avoid economic protectionism and encourage resilience where needed through redundant, trusted sources of truly essential supplies rather than through a move to autarky—a hugely expensive undertaking if it were seriously tried. Meanwhile, broader trade policy aimed at opening markets and creating new rules of the road is likely to be made more difficult by heightened domestic political concerns about globalization.
Long before the pandemic, growth of world trade had fallen behind that of global GDP, and the trend toward globalization of supply chains had slowed or reversed. Calls to “re-shore” medical and pharmaceutical production in the wake of the pandemic could accelerate these trends. The trick for policymakers will be to avoid economic protectionism and encourage resilience where needed through redundant, trusted sources of truly essential supplies rather than through a move to autarky—a hugely expensive undertaking if it were seriously tried. Meanwhile, broader trade policy aimed at opening markets and creating new rules of the road is likely to be made more difficult by heightened domestic political concerns about globalization.
Technology: Whether through a “Zoom effect” reshaping the workplace or in response to demands for new health-surveillance methods, technology—always the great disruptor—is likely to play an even more central role in shaping the economy and society. This has profound implications for policy. What is the right balance between collecting and sharing health data on one hand and protecting personal privacy on the other? To the extent the pandemic accelerates the move toward automation and artificial intelligence, how should government support the jobs and incomes of displaced workers? Should the public sector do more targeted investments in critical sectors and technologies that make us less dependent on others, especially adversaries? Again, policymakers were grappling with these issues long before the pandemic, but the crisis has added a new urgency to these debates.
Whether through a “Zoom effect” reshaping the workplace or in response to demands for new health-surveillance methods, technology—always the great disruptor—is likely to play an even more central role in shaping the economy and society. This has profound implications for policy. What is the right balance between collecting and sharing health data on one hand and protecting personal privacy on the other? To the extent the pandemic accelerates the move toward automation and artificial intelligence, how should government support the jobs and incomes of displaced workers? Should the public sector do more targeted investments in critical sectors and technologies that make us less dependent on others, especially adversaries? Again, policymakers were grappling with these issues long before the pandemic, but the crisis has added a new urgency to these debates.
China: The return of the Middle Kingdom to global prominence over the past few decades has been one of the most disruptive forces in modern history, not least to the global economy. In many ways this has been the most positive form of disruption, whether for the 850 million Chinese citizens who have risen from abject poverty into the middle class or for the billions of consumers around the world who have enjoyed affordable products made in China. But China’s integration into the global economy has also come with costs, including dislocation of millions of manufactured jobs in the advanced world, distortion of competitive markets due to heavy-handed Chinese industrial policies, and threats to international rules and norms from Beijing’s non-transparent and coercive economic statecraft. Concerns about the early handling of the coronavirus in Wuhan will only add to the challenge for U.S. policymakers of finding the right balance between engaging with Beijing and pushing back against it.
The return of the Middle Kingdom to global prominence over the past few decades has been one of the most disruptive forces in modern history, not least to the global economy. In many ways this has been the most positive form of disruption, whether for the 850 million Chinese citizens who have risen from abject poverty into the middle class or for the billions of consumers around the world who have enjoyed affordable products made in China. But China’s integration into the global economy has also come with costs, including dislocation of millions of manufactured jobs in the advanced world, distortion of competitive markets due to heavy-handed Chinese industrial policies, and threats to international rules and norms from Beijing’s non-transparent and coercive economic statecraft. Concerns about the early handling of the coronavirus in Wuhan will only add to the challenge for U.S. policymakers of finding the right balance between engaging with Beijing and pushing back against it.
Climate change: This is one of the few areas in which the pandemic has reversed unhealthy trends in the global economy. Since the start of the year, global carbon dioxide emissions have dropped an estimated 8.6 percent as factories have shut down, planes have been grounded, and commuters in advanced countries have abandoned their cars. But as the global economy returns to something like normal, these gains are unlikely to be sustained. The question is whether policymakers can seize on the temporary reprieve to institute policies that bend the arc of emissions in a more sustained way.
This is one of the few areas in which the pandemic has reversed unhealthy trends in the global economy. Since the start of the year, global carbon dioxide emissions have dropped an estimated 8.6 percent as factories have shut down, planes have been grounded, and commuters in advanced countries have abandoned their cars. But as the global economy returns to something like normal, these gains are unlikely to be sustained. The question is whether policymakers can seize on the temporary reprieve to institute policies that bend the arc of emissions in a more sustained way.
Inequality: There is clear evidence that the costs of the pandemic—whether measured by Covid-19 cases and deaths or lost jobs and incomes—are falling far more heavily on already disadvantaged groups. This has exacerbated the growing gap between rich and poor within both advanced and low-income countries. Why is this on a list of priorities for U.S. international economic policymakers? For one thing, because inequality is a drag on global growth, a point that the International Monetary Fund (IMF) has been stressing over the past few years. Moreover, until Washington addresses the sources of inequality at home, it is unlikely to rebuild the domestic political support for U.S. engagement in the world, no matter how broad its benefits.
These and other disruptive forces will be among the topics explored in a new biweekly series of live webcasts at CSIS called “Economy Disrupted.” In each episode, my colleague Stephanie Segal and I will talk to a prominent economic thinker about one of these challenges and try to make sense of it for non-economists. The web series, kicking off on June 1, will be a marquee offering of the new Economics Program at CSIS. Building on four decades of work in the Simon Chair in Political Economy and including the same strong team, the Economics Program will seek to illuminate the role of economics in international affairs and offer practical policy ideas to enhance U.S. and global prosperity and security. We’re excited to set out on this new journey and hope you will join us.
Matthew P. Goodman is senior vice president and runs the economics program at the Center for Strategic and International Studies (CSIS) in Washington, D.C.
Commentary is produced by the Center for Strategic and International Studies (CSIS), a private, tax-exempt institution focusing on international public policy issues. Its research is nonpartisan and nonproprietary. CSIS does not take specific policy positions. Accordingly, all views, positions, and conclusions expressed in this publication should be understood to be solely those of the author(s).
© 2020 by the Center for Strategic and International Studies. All rights reserved.
| 2020-05-28T00:00:00 |
2020/05/28
|
https://www.csis.org/analysis/economics-all-about-disruption
|
[
{
"date": "2020/05/28",
"position": 93,
"query": "AI economic disruption"
},
{
"date": "2020/05/28",
"position": 92,
"query": "AI economic disruption"
}
] |
Microsoft lays off journalists to replace them with AI | The Verge
|
Microsoft lays off journalists to replace them with AI
|
https://www.theverge.com
|
[
"Tom Warren"
] |
The layoffs are part of a bigger push by Microsoft to rely on artificial intelligence to pick news and content that's presented on MSN.com ...
|
is a senior editor and author of Notepad , who has been covering all things Microsoft, PC, and tech for over 20 years.
Microsoft is laying off dozens of journalists and editorial workers at its Microsoft News and MSN organizations. The layoffs are part of a bigger push by Microsoft to rely on artificial intelligence to pick news and content that’s presented on MSN.com, inside Microsoft’s Edge browser, and in the company’s various Microsoft News apps. Many of the affected workers are part of Microsoft’s SANE (search, ads, News, Edge) division, and are contracted as human editors to help pick stories.
“Like all companies, we evaluate our business on a regular basis,” says a Microsoft spokesperson in a statement. “This can result in increased investment in some places and, from time to time, re-deployment in others. These decisions are not the result of the current pandemic.”
While Microsoft says the layoffs aren’t directly related to the ongoing coronavirus pandemic, media businesses across the world have been hit hard by advertising revenues plummeting across TV, newspapers, online, and more.
The layoffs are happening in the US and UK
Business Insider first reported the layoffs on Friday, and says that around 50 jobs are affected in the US. The Microsoft News job losses are also affecting international teams, and The Guardian reports that around 27 are being let go in the UK after Microsoft decided to stop employing humans to curate articles on its homepages.
Microsoft has been in the news business for more than 25 years, after launching MSN all the way back in 1995. At the launch of Microsoft News nearly two years ago, Microsoft revealed it had “more than 800 editors working from 50 locations around the world.”
Microsoft has gradually been moving towards AI for its Microsoft News work in recent months, and has been encouraging publishers and journalists to make use of AI, too. Microsoft has been using AI to scan for content and then process and filter it and even suggest photos for human editors to pair it with. Microsoft had been using human editors to curate top stories from a variety of sources to display on Microsoft News, MSN, and Microsoft Edge.
| 2020-05-30T00:00:00 |
2020/05/30
|
https://www.theverge.com/2020/5/30/21275524/microsoft-news-msn-layoffs-artificial-intelligence-ai-replacements
|
[
{
"date": "2020/05/30",
"position": 87,
"query": "artificial intelligence layoffs"
},
{
"date": "2020/05/30",
"position": 96,
"query": "artificial intelligence layoffs"
},
{
"date": "2020/05/30",
"position": 91,
"query": "artificial intelligence layoffs"
}
] |
Reskilling for the Age of Automation and AI - NatWest Group - HRD
|
Reskilling for the Age of Automation and AI
|
https://www.hrdconnect.com
|
[
"Hrd Connect"
] |
Reskilling for the Age of Automation and AI - NatWest Group. 1 Min Read. Dr Anna Koczwara, Global Head of Behavioural Science, NatWest Group, ...
|
1 in 3 employees believe their job will not exist in a few years due to advances in AI and automation. The current crisis has only heightened concerns – job security is at an all-time low. At the same time, retaining business continuity by investing in the right skills is more important than ever. Which skills will be most valuable in the age of automation? How do you close the gap between now and the future through upskilling and reskilling your current workforce? Dr Anna Koczwara, Global Head of Behavioural Science, NatWest Group addressed these questions in her masterclass at HRD: A Virtual Experience.
View the full session below.
Subscribe to HRD Connect for daily updates on the future of work, including thought leadership, video interviews, the HRD Live Podcast and more.
Was this article helpful? Yes No
| 2020-06-18T00:00:00 |
2020/06/18
|
https://www.hrdconnect.com/2020/06/18/reskilling-for-the-age-of-automation-and-ai/
|
[
{
"date": "2020/06/18",
"position": 81,
"query": "reskilling AI automation"
},
{
"date": "2020/06/18",
"position": 87,
"query": "reskilling AI automation"
}
] |
Automation and Job Transformation in Canada: Who's at Risk?
|
Automation and Job Transformation in Canada: Who’s at Risk?
|
https://www150.statcan.gc.ca
|
[] |
Results suggest that, overall, 10.6% of Canadian workers were at high risk (probability of 70% or higher) of automation-related job ...
|
Analytical Studies Branch Research Paper Series
Automation and Job Transformation in Canada: Who’s at Risk? View the most recent version. Archived Content Information identified as archived is provided for reference, research or recordkeeping purposes. It is not subject to the Government of Canada Web Standards and has not been altered or updated since it was archived. Please "contact us" to request a format other than those available. Archived This page has been archived on the Web. by Marc Frenette and Kristyn Frank 11F0019M No. 448
Release date: June 29, 2020 More information PDF version
Abstract
Recent significant advances in artificial intelligence have raised questions about the role of workers in an era when robots and algorithms are increasingly able to perform many job duties, including those previously believed to be non-automatable. The COVID-19 pandemic have added to these concerns, as businesses may turn to new automation technology to perform a broader range of work activities traditionally done by human workers. While previous studies have estimated the share of Canadian workers at high risk of automation-related job transformation, this study is the first to examine in great detail the automation risks faced by different groups of workers. This study applies an approach similar to the one developed by Frey and Osborne (2013) and Arntz, Gregory and Zierahn (2016) to Canadian data. Results suggest that, overall, 10.6% of Canadian workers were at high risk (probability of 70% or higher) of automation-related job transformation in 2016, while 29.1% were at moderate risk (probability of between 50% and 70%). Several groups had a relatively higher share of workers who were at high risk, including those who were older (55 or above), had no postsecondary credentials or postsecondary credentials in certain fields, had low literacy or numeracy proficiency, had low employment income, or were employed part time, in small firms, in certain occupations (e.g., Office support occupations), or in the manufacturing sector. One specific finding of interest is that Business, management and public administration and Health and related fields graduates faced the highest automation-related job transformation risks among postsecondary certificate and diploma holders, but they were among the groups facing the lowest risks when looking at postsecondary degree holders.
Executive summary
The recent development of several artificial intelligence applications—such as driverless vehicles, robo-writers and computer-aided medical diagnostics—has led to concerns about the role of human workers in the future workforce. The COVID-19 pandemic has added to these concerns, as businesses may turn to new artificial intelligence technologies to perform work activities not traditionally regarded as automatable, such as social tasks. Although the risk of automation-related job transformation is typically not distributed equally across different groups of workers, the economic consequences of the COVID-19 pandemic may be far-reaching, and could affect workers across a broad range of industries (Muro, Maxim and Whiton 2020). While previous studies have estimated the share of Canadian workers at high risk of automation-related job transformation, this study is the first to examine in great detail the automation risks faced by different groups of workers.
This study adopts a methodology similar to the one developed by Frey and Osborne (2013) and Arntz, Gregory and Zierahn (2016), and applies it to the 2016 Longitudinal and International Study of Adults, Wave 3. Frey and Osborne (2013) estimated the probability that jobs held by workers could be fully automated, based largely on input from artificial intelligence experts. Arntz, Gregory and Zierahn (2016) expanded on this by estimating adjusted automation risks in a model that took into account a broad range of job tasks and worker and firm characteristics.
This study estimates the risk faced by paid Canadian workers, after accounting for tasks, and the risk faced by specific groups of workers. It is important to note that these risk estimates are largely based on the technological feasibility of automating job tasks. There are several reasons why employers may not immediately replace humans with robots, even if it is technologically feasible to do so. These reasons include financial, legal and institutional factors; shortages in complementary skills; and product demand‑side considerations. Consequently, a high risk of automation does not necessarily imply a high risk of job loss. At the very least, it could imply a certain degree of job transformation, which is the terminology used in this study.
Results suggest that, overall, 10.6% of Canadian workers were at high risk (probability of 70% or higher) of automation-related job transformation in 2016, while 29.1% were at moderate risk (probability of between 50% and 70%). Several groups had a relatively higher share of workers who were at high risk, including those who were older (55 or above), had no postsecondary credentials or postsecondary credentials in certain fields, had low literacy or numeracy proficiency, had low employment income, or were employed part time, in small firms, in certain occupations (e.g. Office support occupations), or in the manufacturing sector. One specific finding of interest is that Business, management and public administration and Health and related fields graduates faced the highest automation-related job transformation risks among postsecondary certificate and diploma holders, but they were among the groups facing the lowest risks when looking at postsecondary degree holders.
Future research could estimate the extent to which workers classified as being at risk of automation‑related job transformation were displaced from their job soon thereafter, or participated in retraining. Another avenue for follow‑up research could examine why certain groups of workers face a higher risk of automation-related job transformation. Finally, it could also be useful to investigate how artificial intelligence has resulted in emerging occupations, shifted the composition of existing occupations, or changed the tasks performed by workers.
1 Introduction
Technological advancement has been a feature of developing economies for millennia. With the onset of the industrial revolution in the 18th century, the speed of technological progress began to increase significantly. Most early industrial activities were fairly labour intensive, and new technology facilitated these tasks and allowed workers to focus on more cerebral goals (e.g., operating machines rather than performing the tasks that those machines could now do). This “creative destruction” of jobs has been championed by many economists as a means of achieving higher productivity for the economy as a whole through labour specialization (i.e., machines focus on performing difficult or routine physical tasks at a relatively low cost, while humans focus on tasks that require more judgment or other forms of advanced mental processes).
In the second half of the 20th century, digital technology emerged as an important driver of change in the workplace. The consensus from the literature is that, while the advent of computers in the workplace may have replaced certain jobs linked to routine calculations (e.g., bookkeepers), it has also created considerably more new jobs in areas that complement digital technology (e.g., computer programmers). For empirical evidence, see Autor, Levy and Murnane (2003), and Graetz and Michaels (2018).
More recently, another round of digital advances—this time in the practical applications of artificial intelligenceNote —has facilitated several tasks that were traditionally considered non-automatable. Recent advances in the development of driverless vehicles, robo‑writers and computer-aided medical diagnostics have led to speculation that technology will lead to widespread adoption of new forms of automation in the workplace. The fear by some is that there may be few tasks that humans perform that robots or algorithms cannot perform at a lower cost.
The COVID-19 pandemic has added to these concerns. The closure of workplaces and the susceptibility of workers to the virus may incentivize businesses to test whether new technologies can perform a broader range of work activities, such as social tasks (Leduc and Liu 2020). Moreover, since the COVID-19 pandemic has affected many different industries, the integration of automation technology in industries which may not have traditionally employed such technology may be far-reaching (Muro, Maxim and Whiton 2020). Therefore, the consequences could be felt by a broad range of workers, and at a faster pace than previously expected.
When the changes brought on by new technology will actually happen can only be determined with time. Some researchers have made predictions of occupational growth (Lamb 2016) and skill growth (RBC 2018) based on established forecasting techniques. But even the most carefully chosen statistical methods can fail to accurately predict the future. As an example, the high-tech sector experienced considerable growth throughout the 1990s. At the turn of the 21st century, when the sector was at its peak, it appeared to be the employer of the future. As it turned out, high-tech workers experienced large‑scale permanent layoffs in 2001 (Frenette 2007), while employment in traditional industries—such as construction, and mining, quarrying, and oil and gas extraction—expanded rapidly over the following decade (Statistics Canada 2020).
There are a few known facts about the implementation of automation in the manufacturing sector.Note Data on the use of robotics suggest a global increase in robot density, defined as the number of multipurpose industrial robots in operation per 10,000 people employed in the manufacturing sector. In the Americas, robot density rose by 7% annually, on average, between 2010 and 2016. This was lower than the annual growth rate in Asia (9%), but higher than the rate in Europe (5%). In 2016, Canada was in 13th place internationally with regard to robot density (145 robots in use per 10,000 employees). The Republic of Korea (631) was in first place, followed by Singapore (488) in second place. The United States (189) was in seventh place. Canada was ahead of France (132) in 18th place, and Australia (83) in 21st place. The worldwide average was 74 units per 10,000 employees.
This study will advance knowledge of automation risks in the Canadian workplace by attempting to answer the following question: “Which Canadian workers face greater automation‑related risks?” Understanding who is at risk is important for policy. As technology improves and more tasks become automated, it is likely that different types of workers will be affected along the way. When the earliest forms of technology were being implemented, manual labourers were likely the most affected. As digitization increasingly underpins most new forms of technological advancements, more cerebral tasks (and the human workers performing them) may be affected. Whether these workers will lose their jobs as a result of automation cannot be answered by any empirical approach. That being said, these workers may experience a certain degree of job transformation that results from performing different tasks.
The most often cited work on the degree of automation-related risks for jobs is the study by Frey and Osborne (2013), which estimated the degree to which occupations in the U.S. 2010 Standard Occupational Classification (SOC 2010) were susceptible to automation (or computerization). They found that 47% of all U.S. workers faced a probability higher than 70% of automation within the next 10 or 20 years. One critique of this approach is that it relies largely on an initial binary classification of selected occupations (i.e., fully automatable or not automatable), although some adjustments were made for a small number of tasks associated with each job.
In work for the Organisation for Economic Co-operation and Development (OECD), Arntz, Gregory and Zierahn (2016) estimated an adjusted version of the Frey and Osborne (2013) index that was more thorough in accounting for the fact that occupations comprise different tasks—some of which are automatable. The adjusted version also accounted for differences in firm and individual characteristics within occupations. Based on this approach, the authors found that only 9% of U.S. jobs were at high risk of automation (i.e., probability of automation higher than 70%). The authors also derived estimates for other OECD member countries, and found that 9% of Canadian jobs were at high risk of automation.
The current study will also adjust the Frey and Osborne (2013) index for tasks, similar to the study by Arntz, Gregory and Zierahn (2016). However, it will apply this approach to more recent Canadian data from the Longitudinal and International Study of Adults (LISA), Wave 3, conducted in 2016.Note More importantly, this study will also closely examine differences in automation risk across several worker and firm characteristics.Note
The next section presents the methods, and the following section presents the results. The study then concludes with a brief summary of the results, and with suggestions for follow‑up work in this area.
2 Methods
The objective of this study is to estimate the degree of automation risk faced by different groups of Canadian workers. This involves two steps. The first step is to select a method for determining the risk of automation. The second step is to apply the chosen method to recent Canadian data that are capable of supporting the derivation of the automation risk estimates by various worker and firm characteristics.
Estimating the degree of automation risk among the workforce is an emerging area of research. The study by Frey and Osborne (2013) is the most widely cited work in this area. It was based on the 2010 Occupational Information Network (O*NET) data file, which contains 913 occupations. These occupations correspond closely to the SOC 2010 structure. After six-digit SOC 2010 codes that were missing from O*NET were dropped, 702 codes remained. Working closely with a group of machine-learning and engineering researchers, Frey and Osborne (2013) began by subjectively labelling 70 of the remaining 702 SOC 2010 codes as automatable (1) or not automatable (0). The 70 occupations were hand-picked based on a high degree of confidence, following consultations with the experts.Note Note Next, the relationship between the probability of being automatable and nine O*NET task variables was modelled. These nine task variables captured three “engineering bottlenecks to computerisation” (Frey and Osborne 2013, p. 23), namely, perception and manipulation, creativity, and social intelligence.Note The predicted probabilities were then assigned to the 702 occupations. The O*NET data were then linked to the U.S. Bureau of Labor Statistics’ 2010 Quarterly Census of Employment and Wages to estimate the proportion of the U.S. workforce with jobs that were at high risk (at least 70%) of automation. Overall, 47% of U.S. workers had jobs that fell under this category.
Arntz, Gregory and Zierahn (2016) critiqued the approach of Frey and Osborne (2013) because of the largely binary occupation description. In their view, jobs with the same occupational code may be heterogeneous because of different tasks required, different types of workers performing those tasks, and different firms employing the workers. Frey and Osborne (2013) accounted for differences in only nine tasks, and did not account for worker or employer characteristics. Arntz, Gregory and Zierahn (2016) began by applying the Frey and Osborne (2013) risk probabilities to the 2012 Programme for the International Assessment of Adult Competencies (PIAAC) and found that 38% of U.S. workers had jobs at high risk of automation. Next, they regressed the probabilities on 25 O*NET task variables, and several worker-level and firm-level characteristics, using a fractional response model.Note The predicted probabilities from the model varied not only by occupation, but also by worker within occupations. In contrast to the results of Frey and Osborne (2013), Arntz, Gregory and Zierahn (2016) found that only 9% of U.S. and Canadian workers had jobs that were at high risk of automation.Note
This study’s approach largely follows the one by Arntz, Gregory and Zierahn (2016). First, the Frey and Osborne (2013) automation risk probabilities were assigned to workers in the 2016 LISA , based on their occupation. Since the Frey and Osborne (2013) data are based on the SOC 2010, and LISA data are based on the 2011 National Occupational Classification (NOC 2011), an SOC 2010 to NOC 2011 concordance was applied.Note The matching was based on the similarity of the occupational titles. Of the 500 NOC 2011 codes, 5 had to be dropped since they did not have a U.S. equivalent, while a further 12 had to be dropped because none of the corresponding SOC 2010 codes were populated with automation risk data based on the Frey and Osborne approach. Of the remaining 483 codes, 233 were matched to only one SOC 2010 (i.e., only one six-digit SOC 2010 code matched to one or more four-digit NOC 2011 codes). In these cases, the automation risk associated with the SOC 2010 code by the Frey and Osborne approach was also assigned to the corresponding NOC 2011 code. The remaining 250 NOC 2011 codes matched to more than one SOC 2010 code, in which case an unweighted average of the automation risk values across SOC 2010 codes was taken.Note
Once each of the 483 NOC 2011 codes were assigned an automation risk from the work by Frey and Osborne (2013), they were matched to the 2016 LISA data file by the NOC 2011 code. The sample was limited to paid workersNote aged 18 or older with valid responses for all of the variables used in the analysis (described below). This resulted in a sample of 2,267.
The automation risk from Frey and Osborne (2013) was then regressed on the following 25 task frequencies in LISA , using a probit fractional response model: cooperating or collaborating, sharing information, instructing, making speeches, selling products or services, advising people, planning and organizing own activities, planning and organizing activities of others, planning and organizing own time, persuading or influencing people, negotiating with people, solving problems of less than 5 minutes, solving problems of less than 30 minutes, performing physical work for a long period of time, using skill or accuracy with hands or fingers, reading directions or instructions, reading journals or scholarly publications, reading books, reading manuals or reference materials, writing articles for newspapers or newsletters, filling in forms, using advanced mathematics, using the Internet for work-related issues, using a programming language, and participating in real-time discussions on the Internet.Note Note
From this model, the predicted probability of facing a high risk (70% or more) of automationNote was recovered for each individual in the sample, and used to produce automation risk estimates for the full sample of workers and for various subsamples. Table A.1 in the appendix provides sample statistics for many of the variables used to derive these subsamples, including sex, age, highest level of completed education, field of study (among postsecondary graduates), literacy and numeracy,Note immigration status, disability status, work hours, union membership or collective bargaining agreement coverage, and firm size. In other appendix tables, results are also broken down by occupation, industry and percentile of employment income.
Differences in the automation risk faced by the various groups of workers are unconditionally derived. For example, differences by sex in the risk of automation will result in part from differences by sex in key factors related to automation risk (educational attainment, age, etc.). Therefore, these results will show to what extent the jobs of different groups of workers are at high risk of automation, given the workers’ characteristics. This is useful for identifying workers at greater risk. Further analysis would be needed to uncover the main reasons behind these differences.
Note that all of the estimates used in the study are based on 1,000 bootstrap weights because of the stratified, multistage, multi-phase sampling approach used in the 2016 LISA .Note
The decision to include different variables in the model affects the interpretation of the resulting automation risk estimates. The original Frey and Osborne risk estimates were simply based on the technological feasibility of automating the occupation. However, experts were asked to assign the feasibility at the occupational level. Frey and Osborne (2013) adjusted for some differences in occupational tasks, but Arntz, Gregory and Zierahn (2016) went further. These adjustments for tasks still resulted in automation risk estimates that were based on the technological feasibility of automation, but they were more precise since they were based on job tasks rather than on occupations.
There are several reasons why employers may not immediately replace humans with robots, even if it is technologically feasible to do so.
First, firms must have the capacity to invest in the technology. This may largely depend on firm size, which Arntz, Gregory and Zierahn (2016) take into account. However, firm size may not fully capture the firm’s investment capabilities. Next, there are legal restrictions to consider. This is particularly relevant for government-regulated industries, such as public transportation or healthcare. Even with legal approval, institutional factors may slow down the adoption of automated technology. For example, union contracts may have a no-layoff stipulation or an expensive buyout clause that effectively increases the costs of adopting the new technology. Arntz, Gregory and Zierahn (2016) account for the government sector, but not for unions. Of course, legal restrictions may also affect non-government sectors, depending on the product in question. The new technology may also require skilled human labour to operate, and this may not always be available. Employers must also consider their clientele’s appetite for automated technology. In the end, society may need to reach a certain comfort level with driverless public transit or robo-doctors. Many of the factors above are likely unobservable. Arntz, Gregory and Zierahn (2016) also include worker characteristics, such as sex, age and education level. It is not clear what role these factors play in the model, other than to try to account for unobserved heterogeneity.
In contrast, adjusting for the tasks related to each occupation plays a clearer role in the interpretation of the automation risks estimates. Specifically, the task-adjusted estimates relate strictly to the technological feasibility of automating the occupation as a whole (i.e., by accounting for its inherent tasks). Beyond that, it may be challenging to try to account for the actual probability that firms will adopt automation technology. Consequently, this study opts to simply adjust for a broad range of tasks, and interprets the results as the risk of automation-related job transformation. In other words, workers at higher risk may be more likely to experience job transformation, which may or may not involve job loss. The degree of job transformation will depend on the degree of adoption, based on the firm’s financial ability to invest in the technology, and the legal and societal constraints placed upon them. Adding to this complex decision is the COVID-19 pandemic and the uncertainties it has created around having human workers in the workplace. The threat of further waves of the disease or of future pandemics may expedite investments in new technology in an effort to reduce risks.
3 Results
The distribution of the predicted risk of automation-related job transformation faced by Canadian workers in 2016 appears in Chart 1. The majority of workers faced at least some risk—the predicted risk was at least 10% for 98.2% of the paid workforce. However, only 10.6% were at high risk (70% or more), and about one-quarter (29.1%) of workers were at moderate risk (50% to 70%).
Data table for Chart 1 Data table for Chart 1
Table summary
This table displays the results of Data table for Chart 1. The information is grouped by Predicted risk of automation-related job transformation
(appearing as row headers), Percent of jobs, calculated using percent units of measure (appearing as column headers). Predicted risk of automation-related job transformation
Percent of jobs percent 10% or more 98.2 20% or more 87.3 30% or more 71.9 40% or more 54.4 50% or more 39.7 60% or more 22.1 70% or more 10.6 80% or more 2.6 90% or more 0.0
The remainder of this study will focus on the share of workers in different groups who were at high risk of automation-related job transformation (i.e., 70% or more, in line with Frey and Osborne [2013] and Arntz, Gregory and Zierahn [2016]).Note
The subgroup analysis begins with occupations since they largely determine the automation risks and, therefore, provide good context for the results to follow. Table A.2 in the appendix shows these shares by two-digit NOC 2011 code, and these shares are also shown in descending order in Chart 2.Note
The Office support occupations group—which mainly consists of different types of clerks and receptionists—had the highest concentration of workers who were at high risk of job transformation, at 35.7%. This was almost twice as high as for any other occupation. Next, 20.0%Note of workers in the Service supervisors and specialized service occupations group (food service supervisors, chefs, butchers, hairstylists, tailors, shoe repairers, etc.) were at high risk, followed by 19.7% of workers in Industrial, electrical and construction trades. Also facing above-average risks were the Sales representatives and salespersons—wholesale and retail trade group, at 14.7%; Service representatives and other customer and personal services occupations, at 13.7% (e.g., food and beverage services, travel and accommodation services, security guards, customer service representatives); and Maintenance and equipment operation trades, at 13.2%. Thus, occupations facing above-average risks of automation-related job transformation were largely associated with non‑professional administrative functions (e.g., clerk or salesperson), and various trades, whether in personal services (e.g., butcher) or in heavy industrial trades (e.g., electrician).
At the other end of the spectrum were several professional occupations in which virtually no one faced a high risk of automation-related job transformation. These included Professional occupations in law and social, community and government services; Professional occupations in education services; and Specialized middle management occupations in administrative services, financial and business services, and communications (except broadcasting). All were at 0.0%. Other occupations at low risk included Professional occupations in business and finance (0.8%), and Professional occupations in natural and applied sciences (0.9%).
Data table for Chart 2 Data table for Chart 2
Table summary
This table displays the results of Data table for Chart 2. The information is grouped by Occupation (appearing as row headers), Predicted share of workers, calculated using percent units of measure (appearing as column headers). Occupation Predicted share of workers percent Office support occupations 35.7 Service supervisors and specialized service occupations 20.0 Industrial, electrical and construction trades 19.7 Sales representatives and salespersons—wholesale and retail trade 14.7 Service representatives and other customer and personal services occupations 13.7 Maintenance and equipment operation trades 13.2 Administrative and financial supervisors and administrative occupations 11.3 Technical occupations in health 8.2 Paraprofessional occupations in legal, social, community and education services 6.4 Technical occupations related to natural and applied sciences 4.4 Retail sales supervisors and specialized sales occupations 1.8 Professional occupations in natural and applied sciences 0.9 Professional occupations in business and finance 0.8 Specialized middle management occupations 0.0 Professional occupations in education services 0.0 Professional occupations in law and social, community and government services 0.0
Table A.3 in the appendix shows the predicted share of workers at high risk of automation-related job transformation along various dimensions. Men and women were equally likely to face a high risk (10.7% and 10.6%, respectively), which is interesting since women were more likely to be in Office support occupations, which faced the highest risks. Indeed, 7.8% of women in the analytical sample worked in Office support occupations, compared with only 0.9% of men. This is somewhat counterbalanced by the fact that the occupational group with the third-highest level of risk, Industrial, electrical and construction trades, was male dominated (4.1% of men in the sample worked in such occupations, compared with only 0.3% of women).
The risk of automation-related job transformation varied more by age group (Table A.3 and Chart 3). Specifically, 13.3% of workers between the ages of 18 and 24, and 14.6% of those 55 or older, were in jobs that are at high risk. In contrast, 7.6% of workers aged 25 to 34, and 10.1% of workers aged 35 to 54, were in jobs at high risk. Differences between the middle age groups (ages 25 to 34 and 35 to 54) and the 55-or-older group were statistically significant at 5%. Differences between the 18-to-24 age group and the 35-to-54 age group were not statistically significant. The difference between the 18-to-24 age group and the 25-to-34 age group was significant only at 10%. With this in mind, this chart’s U-shape is perhaps unsurprising. Generally, young workers have not completed their education and, as a result, may end up performing tasks that are routine in nature and are thus highly susceptible to automation. Conversely, older workers have generally been out of school for some time, so they may not have had the opportunity to train for more modern jobs that are less susceptible to automation. In this case, it may be the job that is more at risk than the worker occupying it, since older workers may retire before automation has significantly affected the job.
Data table for Chart 3 Data table for Chart 3
Table summary
This table displays the results of Data table for Chart 3. The information is grouped by Age group (years) (appearing as row headers), Predicted share of workers, calculated using percent units of measure (appearing as column headers). Age group (years) Predicted share of workers percent 18 to 24 13.3 25 to 34 7.6 35 to 54 10.1 55 or older 14.6
Large differences in the probability of facing a high risk of automation-related job transformation also existed by highest level of completed education (Chart 4). Generally, more highly educated workers faced a lower risk. While 33.4% of workers with no certificate, diploma or degree, and 24.1% of workers with a high school diploma, faced a high risk, only 3.6% of workers with a bachelor’s degree and 1.3% with a master’s degree were in the same position.Note The differences between workers with a high school diploma or less and workers with a bachelor’s or master’s degree were statistically significant at 0.1%. Since more highly educated workers were more likely to be professionally employed (see Chart 2), it follows that they faced lower risks of automation-related job transformation.
Data table for Chart 4 Data table for Chart 4
Table summary
This table displays the results of Data table for Chart 4. The information is grouped by Highest level of completed education
(appearing as row headers), Predicted share of workers, calculated using percent units of measure (appearing as column headers). Highest level of completed education
Predicted share of workers percent Master's degree 1.3 First professional degree 6.1 University certificate or diploma above a bachelor's degree 5.5 Bachelor's degree 3.6 University certificate or diploma below a bachelor's degree 6.7 College or CEGEP certificate or diploma 9.9 Trades or apprenticeship certificate 15.4 High school diploma or equivalent 24.1 No certificate, diploma or degree 33.4
Although some results could be generated by field of study, this was limited by small sample sizes (fewer than 50) in certain cases (Table A.3). Nevertheless, some interesting insights emerge. For example, among workers with a postsecondary certificate or diploma, Mathematics, computer and information sciences graduates, and Personal, protective and transportation services graduates were the least likely to be at high risk of automation-related job transformation (both under 7.0%). At the opposite end of the spectrum, Business, management and public administration graduates, and Health and related fields graduates were the most likely to be at high risk (over 12.0% in both cases).
Among workers with a postsecondary degree,Note graduates from every discipline that could be examined faced below-average risks (in all cases, under 5.0% of graduates were at high risk). Workers who graduated from Education (1.0%), Health and related fields (1.8%), and Business, management and public administration (2.2%) programs had the lowest probability of facing a high risk.
Another interesting finding is that Business, management and public administration and Health and related fields graduates faced the highest automation-related job transformation risks among postsecondary certificate and diploma holders, but they were among the groups facing the lowest risks when looking at postsecondary degree holders. This might imply differences in the share of these graduates who landed jobs related to their education, or differences in the types of jobs that were related to the programs.
For example, college business programs are varied, but include office administration, which is part of Office support occupations (and ranked highest in automation risk according Chart 2).Note
Literacy and numeracy are also important factors in the risk of automation-related job transformation. Since both are so highly correlated with the level of educational attainment, these factors are estimated by level of education (Table A.3). Among workers with no postsecondary qualifications, or with a postsecondary certificate, diploma or degree, those with a proficiency level of 3 or above (out of a maximum level of 5) were considerably less likely to be at high risk of automation-related job transformation, although the results are not always statistically significant. All results were significant at the 5% level, with the exception of numeracy among those with no postsecondary qualifications, and literacy among those with a postsecondary degree (both not significant at 10%).
The automation risks are also broken down in Table A.3 by immigration status, disability status and union membership (or coverage by a collective bargaining agreement). The predicted automation risks between the various categories were small and, in each case, not statistically significant at the 10% level.
The remaining work-related characteristics shown in Table A.3 reveal some interesting differences. For example, 25.7% of part-time workers were at high risk of automation-related job transformation, compared with only 8.7% of full-time workers (the difference was significant at 0.1%).
Workers at high risk were also more likely to earn low employment income. Approximately one-quarter (26.8%) of workers in the bottom 10% of the distribution of employment income were at high risk. In contrast, only 2.1% of workers in the top 10% of the employment income distribution were at high risk. In fact, there is a clear, negative and monotonic relationship between employment income and the probability of being at high risk of automation-related job transformation (Chart 5). All differences between those in the bottom 10% of the distribution and the other groups were statistically significant at the 1% level, with the exception of those in the second-to-bottom group (between the 10th and 25th percentiles— significant at 5%).
Data table for Chart 5 Data table for Chart 5
Table summary
This table displays the results of Data table for Chart 5. The information is grouped by Percentile of employment income (appearing as row headers), Predicted share of workers, calculated using percent units of measure (appearing as column headers). Percentile of employment income Predicted share of workers percent Below 10th 26.8 10th to below 25th 16.6 25th to below 50th 13.7 50th to below 75th 5.5 75th to below 90th 3.1 90th or above 2.1
Adopting automation-enabled technology in the workplace may involve considerable financial investment by firms. Larger firms may have an advantage in securing the capital stock required. Therefore, automation may already be in place in those firms, leaving human workers to perform non-automatable tasks. The results shown in Chart 6 are mostly consistent with this line of thinking, as 14.9% of workers in small firms (those with 10 employees or fewer) faced a high risk of automation-related job transformation, compared with only 8.3% of workers in large firms (firms with 1,000 employees or more). This difference was statistically significant at the 5% level. However, if firms with 10 employees or fewer are excluded, there is little to no relationship between firm size and the probability of facing a high risk of automation-related job transformation.
Data table for Chart 6 Data table for Chart 6
Table summary
This table displays the results of Data table for Chart 6. The information is grouped by Firm size(number of employees at place of work) (appearing as row headers), Predicted share of workers, calculated using percent units of measure (appearing as column headers). Firm size (number of employees at place of work)
Predicted share of workers percent 1 to 10 14.9 11 to 50 8.6 51 to 250 11.2 251 to 1,000 9.8 More than 1,000 8.3
The differences in the risks faced by workers in various industries were also considerable (Chart 7). For example, workers in the Manufacturing industry faced the highest risk (26.6% probability of facing a high risk), which was significantly higher than that of workers in all other industries at the 5% level, with the exception of workers in Accommodation and food services (15.4%, significantly different from Manufacturing at the 10% level). At the opposite end of the spectrum, groups with a low share of workers at high risk included those in Information and cultural industries (2.8%); Public administration (3.7%); Educational services (4.2%); and Finance and insurance, real estate and rental and leasing (4.8%).
Data table for Chart 7 Data table for Chart 7
Table summary
This table displays the results of Data table for Chart 7. The information is grouped by Industry (appearing as row headers), Predicted share of workers, calculated using percent units of measure (appearing as column headers). Industry Predicted share of workers percent Manufacturing 26.6 Accommodation and food services 15.4 Transportation and warehousing 14.5 Wholesale and retail trade 13.4 Health care and social assistance 12.0 Construction 8.4 Professional, scientific and technical services 7.2 Other services 5.6 Finance and insurance, real estate and rental and leasing 4.8 Educational services 4.2 Public administration 3.7 Information and cultural industries 2.8
4 Conclusion
The goal of this study was to identify the characteristics of Canadian workers at high risk of automation‑related job transformation, based on an approach similar to the one developed by Frey and Osborne (2013) and Arntz, Gregory and Zierahn (2016).
Overall, 10.6% of Canadian workers were at high risk (probability of 70% or higher) of automation-related job transformation in 2016, while 29.1% were at moderate risk (probability of between 50% and 70%). Several groups had a relatively higher share of workers who were at high risk, including those who were older (55 or above), had no postsecondary credentials or postsecondary credentials in certain fields, had low literacy or numeracy proficiency, had low employment income, or were employed part time, in small firms, in certain occupations (e.g., Office support occupations), or in the manufacturing sector. One specific finding of interest is that Business, management and public administration and Health and related fields graduates faced the highest automation-related job transformation risks among postsecondary certificate and diploma holders, but they were among the groups facing the lowest risks when looking at postsecondary degree holders.
Since these risks are based solely on the feasibility of adopting technology for automation, it is unclear how these results relate to the probability of job loss. Additionally, the extent to which businesses invest in automation technologies as a response to the COVID-19 pandemic is still unknown. Therefore, a useful next step for research would be to estimate the extent to which workers classified as being at risk of automation-related job transformation were displaced from their job soon thereafter. Moreover, how many of those affected workers adjusted by retraining, as opposed to finding another job directly?
Future research could also investigate the underlying reasons why the risk of automation is higher among certain groups of workers.
Finally, it could also be useful to investigate how artificial intelligence has resulted in emerging occupations, shifted the composition of existing occupations, or changed the tasks performed by workers.
5 Appendix: Tables
Table A.1
Characteristics of full sample of workers
Table summary
This table displays the results of Characteristics of full sample of workers Statistics, calculated using percent, mean and number units of measure (appearing as column headers). Statistics percent Female 51.3 Age group (years) 18 to 24 6.0 25 to 34 21.2 35 to 54 53.1 55 or older 19.6 Highest level of completed education No certificate, diploma or degree 2.8 High school diploma or equivalent 16.6 Trades or apprenticeship certificate 9.0 College or CEGEP certificate or diploma 26.9 University transfer program 0.2 University certificate or diploma below a bachelor's degree 4.3 Bachelor's degree 23.3 University certificate or diploma above a bachelor's degree 4.5 First professional degree 2.2 Master's degree 9.1 Doctoral degree 1.1 mean Literacy 293.3 Numeracy 285.6 percent Immigration status Canadian-born 79.4 Long-term immigrant (10 or more years in Canada) 13.7 Recent immigrant (less than 10 years in Canada) 6.9 Disabled 14.3 Part-time worker 11.6 Union member or covered by a collective bargaining agreement 29.3 Firm size (number of employees at place of work) 1 to 10 20.2 11 to 50 31.0 51 to 250 25.4 251 to 1,000 13.7 More than 1,000 9.7 number Sample size 2,267
Table A.2
Predicted share of workers at high risk of automation-related job transformation, by occupation
Table summary
This table displays the results of Predicted share of workers at high risk of automation-related job transformation. The information is grouped by Occupation title (appearing as row headers), Predicted share of workers, calculated using percent and bootstrap standard error units of measure (appearing as column headers). Occupation title Table A.2 Note 1 Predicted share of workers percent bootstrap standard error Office support occupations 35.7 6.1 Service supervisors and specialized service occupations 20.0 7.8 Industrial, electrical and construction trades 19.7 7.9 Sales representatives and salespersons—wholesale and retail trade 14.7 4.1 Service representatives and other customer and personal services occupations 13.7 4.1 Maintenance and equipment operation trades 13.2 4.8 Administrative and financial supervisors and administrative occupations 11.3 2.6 Technical occupations in health 8.2 3.4 Paraprofessional occupations in legal, social, community and education services 6.4 3.2 Technical occupations related to natural and applied sciences 4.4 2.0 Retail sales supervisors and specialized sales occupations 1.8 1.3 Professional occupations in natural and applied sciences 0.9 0.9 Professional occupations in business and finance 0.8 0.7 Specialized middle management occupations Table A.2 Note 2 0.0 0.0 Professional occupations in education services 0.0 0.0 Professional occupations in law and social, community and government services 0.0 0.0
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| 2020-06-29T00:00:00 |
https://www150.statcan.gc.ca/n1/pub/11f0019m/11f0019m2020011-eng.htm
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Artificial intelligence's great impact on low and middle-skilled jobs
|
Artificial intelligence’s great impact on low and middle-skilled jobs
|
https://www.bruegel.org
|
[
"Laura Nurski",
"Mia Hoffmann",
"Giuseppe Porcaro",
"Georgios Petropoulos"
] |
In contrast, AI is highly likely to significantly alter not only middle-skilled jobs, but also low-skill employment. Moreover, while the high ...
|
The academic literature suggests that, in the past decades, technological progress has led to job polarisation in European Union countries. While computer technologies and robots have replaced, to some extent, routine middle-skilled jobs such as machine operation, construction work or administrative work, they have also led to an increase in complementary, non-routine high-skilled jobs (eg managers, professionals) and in low-skilled jobs (eg agriculture, cleaning and personal care services). However, our new research suggests that the new technologies that have emerged since 2010 – artificial intelligence and machine learning – are set to change drastically the job landscape over the next few decades. These technologies are likely to have a deeper impact across a wider range of jobs and tasks, including possible destruction of low-skilled jobs.
(...) new technologies that have emerged since 2010 – artificial intelligence and machine learning – are set to change drastically the job landscape over the next few decades. These technologies are likely to have a deeper impact across a wider range of jobs and tasks, including possible destruction of low-skilled jobs.
Artificial intelligence (AI) systems are able to perform tasks that involve decision-making, therefore changing the impact of automation on the workforce. AI-powered technologies can now retrieve information, coordinate logistics, handle inventories, prepare taxes, provide financial services, translate complex documents, write business reports, prepare legal briefs and diagnose diseases. Moreover, they are set to become much better at these tasks in the next few years thanks to machine learning (ML): computers fed by big data can learn, practice skills and ultimately improve their own performances and perform their assigned tasks more efficiently.
Our new working paper evaluates the ‘probability of automation’ for different jobs, using data from 24 European countries. This probability is initially computed at the job task level and then aggregated at the occupational level (Table 1). Since each job consists of a variety of tasks, with different potential for automation, the probability of automation at the job level does not necessarily mean the destruction of jobs, but rather whether automation can significantly transform the nature of those jobs.
Table 1: European jobs with the highest and lowest probabilities of automation
Source: Brekelmans and Petropoulos (2020) based on Nedelkoska and Quintini (2018).
We use this measure of automation in an aggregate framework where jobs are grouped into three different categories of skill: low, middle and high-skilled jobs. Figure 1 shows the results.
Figure 1: Exposure to automation of different skill groups
Source: Brekelmans and Petropoulos (2020).
These results suggest that artificial intelligence and machine learning will have different impacts compared to computer and robotic technologies, which caused job polarisation (drop in routine middle-skilled jobs and increase in low-skilled jobs). In contrast, AI is highly likely to significantly alter not only middle-skilled jobs, but also low-skill employment. Moreover, while the high skilled are relatively less at risk from AI and ML-induced transformation, its impact is still non-negligible for these jobs.
The results also suggest a future transformation of work. In middle and low-skilled jobs, AI systems will complete the easily automated tasks while humans continue to perform those that cannot be automated. A high probability of automation may also be associated with the creation of new tasks and jobs though the productivity gains from adopting AI technologies, but these jobs and tasks will most likely be high-skilled.
The transformative nature of AI and ML requires proactive measures to re-design labour markets. Countries with high degrees of labour flexibility, high quality science education and less pervasive product market regulations tend to have higher skill-oriented job structures and are therefore less exposed to labour transformation due to automation.
The transformative nature of AI and ML requires proactive measures to re-design labour markets. The workforce needs to be prepared for the upcoming changes, while the efficiency gains from these technologies should be harnessed. Countries with high degrees of labour flexibility, high quality science education and less pervasive product market regulations tend to have higher skill-oriented job structures and are therefore less exposed to labour transformation due to automation.
This Blog was produced within the project "Future of Work and Inclusive Growth in Europe", with the financial support of the Mastercard Center for Inclusive Growth.
Recommended citation
Brekelmans S., G. Petropoulos (2020), 'Artificial intelligence’s great impact on low and middle-skilled jobs', Bruegel Blog, 29 June, available at https://bruegel.org/2020/06/artificial-intelligences-great-impact-on-low-and-middle-skilled-jobs/
| 2020-06-29T00:00:00 |
https://www.bruegel.org/blog-post/artificial-intelligences-great-impact-low-and-middle-skilled-jobs
|
[
{
"date": "2020/06/29",
"position": 81,
"query": "AI job creation vs elimination"
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"date": "2020/06/29",
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"query": "AI job creation vs elimination"
},
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"date": "2020/06/29",
"position": 81,
"query": "AI job creation vs elimination"
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"query": "AI job creation vs elimination"
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"position": 93,
"query": "AI job creation vs elimination"
}
] |
|
Upskilling Workers for the AI and Automation Age - Accurate Skills
|
Upskilling Workers for the AI and Automation Age
|
https://www.accurateskills.com
|
[] |
Nationwide's large investment in upskilling and reskilling its workforce in the new era of artificial intelligence (AI) and automation puts it ...
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In a knowledge-based economy, companies upgrade their “capital” not by overhauling outdated machinery but by upskilling their workforce. That’s because human capital is increasingly the most important asset companies possess. And while humans aren’t as static as machinery, they still need to be retooled and upgraded.
Training Employees in Digital Literacy
Before the COVID-19 pandemic hit, Nationwide announced it would be spending $160 million during the next 5 years to offer all of its 28,000 U.S. employees digital literacy and “future capabilities” training.
The company listed automation and other tech advancements as key factors in launching the new program, which is aimed at preparing its workforce for the increasingly tech-heavy future economy.
Nationwide’s large investment in upskilling and reskilling its workforce in the new era of artificial intelligence (AI) and automation puts it alongside other major employers when it comes to making big investments in human capital. Last July, Amazon said it would commit $700 million to upskilling its workforce by 2020.
Technology Creating New Opportunities
While AI and automation certainly can and will replace some jobs, many overlook the fact that technology also creates new opportunities. Technology can help eliminate some of the more tedious tasks traditionally performed by humans, but advanced technologies still need humans to develop, maintain, improve, and operate them.
In a recent interview with Built In, Sean Chou, CEO of AI start-up Catalytic, argues that successfully leveraging advanced AI, automation, and other technologies requires a lot of skilled workers. And, Chou adds, the more advanced the technology becomes, the more people will be needed.
Chou explains, “So you move from worrying about the impact of high technology to actually helping to create the technology. When you look at AI, there’s this nonstop need for training, for data, for maintenance, for taking care of all the exceptions that are happening. How do we monitor AI? How do we train it? How do we make sure that AI’s not running amok? Those are all going to become new jobs.”
Considering the Jobs of the Future
Nationwide, Amazon, and other big companies recognize the advantages new technological advancements in AI and automation can provide. They also know that current workforces are generally unprepared to successfully utilize those advancements. Upskilling the workforce to more effectively leverage the boons of new technology can be an important benefit for employees and employers alike.
| 2020-07-09T00:00:00 |
2020/07/09
|
https://www.accurateskills.com/post/upskilling-workers-for-the-ai-and-automation-age
|
[
{
"date": "2020/07/09",
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"query": "reskilling AI automation"
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"date": "2020/07/09",
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] |
A new study measures the actual impact of robots on jobs. It's ...
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A new study measures the actual impact of robots on jobs. It’s significant.
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https://mitsloan.mit.edu
|
[
"Sara Brown"
] |
The researchers found that for every robot added per 1,000 workers in the U.S., wages decline by 0.42% and the employment-to-population ratio ...
|
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Machines replacing humans in the workplace has been a perpetual concern since the Industrial Revolution, and an increasing topic of discussion with the rise of automation in the last few decades. But so far hype has outweighed information about how automation — particularly robots, which do not need humans to operate — actually affects employment and wages.
The recently published paper, “Robots and Jobs: Evidence from U.S. Labor Markets," by MIT professor Daron Acemoglu and Boston University professor Pascual Restrepo, PhD ’16, finds that industrial robots do have a negative impact on workers.
The researchers found that for every robot added per 1,000 workers in the U.S., wages decline by 0.42% and the employment-to-population ratio goes down by 0.2 percentage points — to date, this means the loss of about 400,000 jobs. The impact is more sizable within the areas where robots are deployed: adding one more robot in a commuting zone (geographic areas used for economic analysis) reduces employment by six workers in that area.
To conduct their research, the economists created a model in which robots and workers compete for the production of certain tasks.
AI at Work Research and insights powering the intersection of AI and business, delivered monthly. Email Leave this field blank
Industries are adopting robots to various degrees, and effects vary in different parts of the country and among different groups — the automotive industry has adopted robots more than other sectors, and workers who are lower and middle income, perform manual labor, and live in the Rust Belt and Texas are among those most likely to have their work affected by robots.
“It’s obviously a very important issue given all of the anxiety and excitement about robots,” Acemoglu said. “Our evidence shows that robots increase productivity. They are very important for continued growth and for firms, but at the same time they destroy jobs and they reduce labor demand. Those effects of robots also need to be taken into account.”
“That doesn't mean we should be opposed to robots, but it does imply that a more holistic understanding of what their effects are needs to be part of the discussion … automation technologies generally don't bring shared prosperity by themselves,” he said. “They need to be combined with other technological changes that create jobs.”
Industrial robots are automatically controlled, reprogrammable, multipurpose machines that can do a variety of things like welding, painting, and packaging. They are fully autonomous and don’t need humans to operate them. Industrial robots grew fourfold in the U.S. between 1993 and 2007, Acemoglu and Restrepo write, to a rate of one robot per thousand workers. Europe is slightly ahead of the U.S. in industrial robot adoption; the rate there grew to 1.6 robots per thousand workers during that time span.
Improvements in technology adversely affect wages and employment through the displacement effect, in which robots or other automation complete tasks formerly done by workers. Technology also has more positive productivity effects by making tasks easier to complete or creating new jobs and tasks for workers. The researchers said automation technologies always create both displacement and productivity effects, but robots create a stronger displacement effect.
Acemoglu and Restrepo looked at robot use in 19 industries, as well as census and American Community Survey data for 722 commuting zones, finding a negative relationship between a commuting zone’s exposure to robots and its post-1990 labor market outcomes.
6 Share
Adding one robot to a geographic area reduces employment in that area by six workers.
Between 1990 and 2007, the increase in robots (about one per thousand workers) reduced the average employment-to-population ratio in a zone by 0.39 percentage points, and average wages by 0.77%, compared to commuting zones with no exposure to robots, they found. This implies that adding one robot to an area reduces employment in that area by about six workers.
But what happens in one geographic area affects the economy as a whole, and robots in one area can create positive spillovers. These benefits for the rest of the economy include reducing the prices of goods and creating shared capital income gains. Including this spillover, one robot per thousand workers has slightly less of an impact on the population as a whole, leading to an overall 0.2 percentage point reduction in the employment-to-population ratio, and reducing wages by 0.42%. Thus, adding one robot reduces employment nationwide by 3.3 workers.
In a separate study of robot adoption in France, Acemoglu and his co-authors found that French manufacturing firms that added robots became more productive and profitable, but that increases in robot use led to a decline in employment industrywide.
Disproportionate impacts
The impact of robots varies among different industries, geographic areas, and population groups. Unsurprisingly, the effect of robots is concentrated in manufacturing. The automotive industry has adopted robots more than any other industry, the researchers write, employing 38% of existing robots with adoption of up to 7.5 robots per thousand workers.
The electronics industry employs 15% of robots, while plastics and chemicals employ 10%. Employees in these industries saw the most negative effects, and researchers also estimate negative effects for workers in construction and retail, as well as personal services.
While the automotive industry adopted robots at a quicker pace and to a greater degree than other sectors, that industry did not drive the study’s results. The impact of robots was consistent when that industry was taken out of the equation, the researchers write.
Robots are most likely to affect routine manual occupations and lower and middle class workers, and particularly blue-collar workers, including machinists, assemblers, material handlers, and welders, Acemoglu and Restrepo write. Both men and women are affected by adoption of robots, though men slightly more. For men, impacts are seen most in manufacturing jobs. For women, the impacts were seen most in non-manufacturing jobs.
Robots negatively affect workers at all education levels, though workers without college degrees were impacted far more than those with a college degree or more. The researchers also found robot adoption does not have a positive effect on workers with master’s or advanced degrees, which could indicate that unlike other technology, industrial robots are not directly complementing high-skill workers.
Some parts of the United States saw relatively small adoption of robots, while in other states, including Kentucky, Louisiana, Missouri, Texas, and Virginia, robots have been adopted more along the order of two to five robots per thousand workers. In some parts of Texas, that number goes up to five to 10 per thousand workers, the researchers found. Detroit was the commuting zone with the highest exposure to robots.
Overall, robots have a mixed effect: replacing jobs that relatively high-wage manufacturing employees used to perform, while also making firms more efficient and more productive, Acemoglu said. Some areas are most affected by the mixed impact of robots. “In the U.S., especially in the industrial heartland, we find that the displacement effect is large,” he said. “When those jobs disappear, those workers go and take other jobs from lower wage workers. It has a negative effect, and demand goes down for some of the retail jobs and other service jobs.”
Acemoglu and Restrepo emphasize that looking at the future effect of robots includes a great deal of uncertainty, and it is possible the impact on employment and wages could change when robots become more widespread. Industries adopting more robots over the last few decades could have experienced other factors, like declining demand or international competition, and commuting zones could be affected by other negative shocks.
But the researchers said their paper is the first step in exploring the implications of automation, which will become increasingly widespread. There are relatively few robots in the U.S. economy today and the economic impacts could be just beginning.
Robotic technology is expected to keep expanding, with an aggressive scenario predicting that robots will quadruple worldwide by 2025. This would mean 5.25 more robots per thousand workers in the U.S., and by the researchers’ estimate, a 1 percentage point lower employment-to-population ratio, and 2% lower wage growth between 2015 and 2025. In a more conservative scenario, the stock of robots could increase slightly less than threefold, leading to a 0.6 percentage point decline in the employment-to-population ratio and 1% lower wage growth.
The economic crisis spurred by the COVID-19 pandemic will further exacerbate the good and bad impacts of robots and technology, Acemoglu said. “The good because we are really dependent on digital technologies. If we didn't have these advanced digital technologies, we wouldn't be able to use Zoom or other things for teaching and teleconferencing. We would not be able to keep factories going in many areas because workers haven't fully gotten back to work,” he said. “But at the same time, by the same token, this increases the demand for automation. If the automation process was going too far or had some negative effects, as we find, then those are going to get multiplied as well. So we need to take those into account.”
Read "Robots and Jobs: Evidence from U.S. Labor Markets"
| 2020-07-29T00:00:00 |
2020/07/29
|
https://mitsloan.mit.edu/ideas-made-to-matter/a-new-study-measures-actual-impact-robots-jobs-its-significant
|
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] |
Research summary: Changing My Mind About AI, Universal Basic ...
|
Research summary: Changing My Mind About AI, Universal Basic Income, and the Value of Data
|
https://montrealethics.ai
|
[] |
In the article “Changing my Mind about AI, Universal Basic Income, and the Value of Data”, author Vi Hart explores the attractive idea of UBI ...
|
Summary contributed by Sneha Deo, a computer scientist (PM @ Microsoft), grassroots organizer, and musician based in Seattle, WA.
*Authors of full paper & link at the bottom
As Artificial Intelligence grows more ubiquitous, policy-makers and technologists dispute what will happen. The resulting labor landscape could lead to an underemployed, impoverished working class; or, it could provide a higher standard of living for all, regardless of employment status. Recently, many claim the latter outcome will come to pass if AI-generated wealth can support a Universal Basic Income – an unconditional monetary allocation to every individual. In the article “Changing my Mind about AI, Universal Basic Income, and the Value of Data”, author Vi Hart examines this claim for its practicality and pitfalls.
Through this examination, the author deconstructs the belief that humans are rendered obsolete by AI. The author notes this belief benefits the owners of profitable AI systems, allowing them to acquire the on-demand and data labor they need at unfairly low rates – often for less than a living wage or for free. And although a useful introduction to wealth redistribution, UBI does not address the underlying dynamics of this unbalanced labor market. Calling for the fair attribution of prosperity, the author proposes an extension to UBI: a model of compensation that assigns explicit value to the human labor that keeps AI systems running.
Full summary:
Artificial Intelligence may soon become powerful enough to change the landscape of work. When it does, will it devastate the job market and widen the wealth gap, or will it lay the foundation for a technological utopia where human labor is no longer required? A potential intersection between these seemingly opposed theories has developed into an increasingly popular idea in the past 5 years: the idea that human work may become obsolete, but that AI will generate such excess wealth that redistribution in the form of Universal Basic Income is possible. In the article “Changing my Mind about AI, Universal Basic Income, and the Value of Data”, author Vi Hart explores the attractive idea of UBI and AI – long prophesied by tech industry leaders – and weighs its practicality and pitfalls.
Universal Basic Income is a program that provides every individual with a standardized unconditional income. It has been presented as a salve to the existential problem of massive unemployment as AI replaces human workers. It could reduce financial dependence on traditional jobs, freeing individuals to pursue meaningful (rather than market-driven) skill development. And although UBI may appear costly, the relative cheapness of AI labor could generate capital for redistribution.
While it might seem an ideal solution at first glance, UBI doesn’t address the most dangerous threat presented by AI: the devaluation of the human labor that makes AI programs work.
For the past 5 years, the tech elite have justified the devaluation of the human worker by claiming artificial intelligence will be orders of magnitude more productive than manual work. They extend this line of reasoning by idealizing “pure” AI, which will move beyond the need for human participation at all.
But this rhetoric is untrue: human contributions are necessary inputs for AI to make decisions. AI is only as useful as the “collective intelligence” it draws upon – human-generated data collected knowingly or unknowingly. The gig economy of producing data through online marketplaces like MTurk is unregulated and can pay less than a living wage. This is, in part, because the value of data is set by an unbalanced data market (a monopsony), as many data are collected freely in exchange for use of online services.
In addition to their role in data creation, human workers participate in customer service, delivery, and other on-demand tasks under the guise of full automation. Call center workers, content moderators, and other humans invisibly fill in the “last mile” of decisions that AI systems cannot make. This illusion helps justify the artificially low value of data labor, even though that labor will generate massive wealth for corporations.
In sum, a marketplace radically transformed by AI will likely drive workers’ perceived worth down – and UBI may not reverse the harmful results. The utopian vision for AI and UBI, touted by the tech elite, deflects responsibility from corporations to pay for the data labor that is so valuable to them. The author proposes a solution that goes beyond UBI to establish “data dignity”: fair compensation for data labor in a balanced marketplace. Above all else, individuals must be recognized and valued for their data. They must be able to reason on the value of their contributions and make the choice to contribute.
Original paper by Vi Hart: https://theartofresearch.org/ai-ubi-and-data/
| 2020-08-17T00:00:00 |
2020/08/17
|
https://montrealethics.ai/research-summary-changing-my-mind-about-ai-ubi-and-the-value-of-data/
|
[
{
"date": "2020/08/17",
"position": 52,
"query": "universal basic income AI"
}
] |
Why Robots and AI May Not Herald a Job Apocalypse | Stanford HAI
|
Why Robots and AI May Not Herald a Job Apocalypse
|
https://hai.stanford.edu
|
[
"Edmund L. Andrews"
] |
He found, in some, the introduction of robots and AI initially replaced human workers, but often those industries generated new jobs that at ...
|
If you worry that robots and artificial intelligence will cause mass unemployment, new studies of three different industries offer hope that the end of work isn’t yet at hand.
Stanford scholar Yong Suk Lee examined impact of AI and robotics on three sectors: manufacturing, retail banking, and nursing homes. He found, in some, the introduction of robots and AI initially replaced human workers, but often those industries generated new jobs that at least partially offset the losses. In other cases, he saw automation appear to spur a small increase in total jobs.
Driving this change? Overall increases in productivity due to AI adoption may allow businesses to grow.
“I wouldn’t say these technologies ultimately add jobs, but the impact of robots often evolves over time from replacing human workers to augmenting them,” says Lee, a center fellow at Stanford’s Freeman Spogli Institute for International Studies. “There are also productivity gains that create opportunities for existing and new occupations,” adds Lee, who co-authored all three pre-peer reviewed papers and spoke recently about his findings at a Stanford Institute for Human-Centered Artificial Intelligence (HAI) workshop.
Examining Manufacturing
Automobile and electronics manufacturing have accounted for about 90 percent of the industrial robots purchased in the United States. Lee teamed with Jong Hyun Chung, a recent graduate of Stanford who is now an assistant economics professor at Auburn University, to see how the adoption of robots changed employment in U.S. localities. Drawing on data about annual robot shipments broken down by industry as well as Census Bureau data on the industrial make-up of localities nationwide, the researchers examined the impact of an area’s robot exposure on local job trends.
Sure enough, they found that robots did initially replace a significant number of manufacturing workers. From 2005 to 2010, they estimated, the addition of one robot per 1,000 workers led to a decline in the local employment-to-working-age population ratio by 3.06 percent and in wages by 6.8 percent. In those first five years, this amounted to one additional robot replacing about 45 factory jobs.
But that grim relationship later reversed direction. From 2011 to 2016, each additional robot appears to have spurred a gain of 13 to 14 jobs — most of them in manufacturing but also in “spillover” areas like the service sector. In those later years, an additional one robot per 1,000 workers correlated with an increase in the local employment-to-working-age population ratio by 0.78 percentage points.
Why would robots be bad for jobs in the early years but good for jobs later on?
Lee thinks there are several possible reasons. For one thing, it takes time for robots to actually deliver higher productivity, which in turn can allow a company to expand. Equally important, Lee says, companies are increasingly using robots to augment human workers. Car companies, for example, are experimenting with collaborative robots, or “co-bots,” which take on physically demanding tasks while humans focus on detail work, such as customization.
Bank AI
Banks have been among the most active adopters of artificial intelligence, using it in loan underwriting, fraud detection, customer support, and many other areas. To measure AI’s impact on banking jobs, Lee teamed up on a second study of job postings at regional banks that had ramped up their use of AI to keep up with nationwide rivals.
That study, co-authored with Stanford Graduate School of Business assistant professor Jung Hoi Choi and economics graduate student Yeji Kee, found that heavy recruitment in AI was indeed tied to a drop in demand for lower-skilled jobs like bank tellers. However, the researchers also saw an increase in other higher-skilled jobs, such as financial analysts and managers that more than made up for the loss. In fact, their initial results find that a 1 percent increase in a bank’s AI job postings was tied to a small .36 percent net gain in its total employment.
Here too, Lee says, the increase in jobs was probably spurred by higher productivity. On average, the researchers found, the regional banks that embraced AI expanded their geographic reach and took on a wider variety of borrowers. They took on slightly higher risks, but they increased their net interest income. However, Lee notes that this study is still in its preliminary stages and that the team is further probing into AI’s impact on the demand for skill.
Nursing Home Robots
Faced with an aging population and a low unemployment rate, Japanese nursing homes have scrambled to retain enough skilled workers and in some cases have turned to technology to fill the gaps. Japanese nursing homes use both stand-alone and wearable robots to help caregivers move patients. They also use robots to monitor residents’ vital signs and remind them to take medications. Some use friendly humanoids to provide comfort and conversation to patients with dementia.
Lee, with Stanford FSI senior fellow Karen Eggleston and economics professor Toshiaki Iizuka of the University of Tokyo, teamed to study how these robots impacted overall staffing.
Because many Japanese municipalities subsidize robot purchases by nursing homes, the researchers used subsidies as a proxy for robot usage and then correlated that data with survey data on about 1,000 Japanese institutions. The robots had no significant impact on nursing homes’ head count but potentially increased demand for non-regular caregivers on more flexible contracts.
To be sure, Lee says, nursing homes in Japan may be an unusual case. In other sectors in other nations, such as logistics and warehousing in the United States, robots could lead to permanent job reductions. But that probably won’t be the end of the story.
“What will ultimately matter is whether there will be entirely new occupations, what economists call the ‘reinstatement effect,’” he says. “When the automobile was invented, we suddenly had a new demand for drivers. Now we’ll have to see if autonomous cars, which are a real likelihood, will create demand for other new occupations. We just don’t know yet.”
Stanford HAI's mission is to advance AI research, education, policy and practice to improve the human condition. Learn more.
| 2020-08-19T00:00:00 |
https://hai.stanford.edu/news/why-robots-and-ai-may-not-herald-job-apocalypse
|
[
{
"date": "2020/08/19",
"position": 64,
"query": "robotics job displacement"
}
] |
|
A short history of jobs and automation - The World Economic Forum
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A short history of jobs and automation
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https://www.weforum.org
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[] |
But humans were on hand to supervise the machines. As the technology has improved, the range of jobs passed on to robots has expanded to cover ...
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One-third of all jobs could be at risk of automation in the next decade.
People with low educational attainment are most at risk.
Previous waves of mechanization have caused difficulty and anxiety too.
Technology could create millions more jobs than it displaces.
Millions of people across the globe have lost their jobs to the COVID-19 crisis. In major economies like the US, some of those jobs have already been recovered, although “there is a long road ahead,” as Bank of America economist Michelle Meyer told The New York Times.
But for many people, the job they used to do might not be coming back. And increasingly, as employers battle with the challenges of the pandemic, this could be due to automation.
By the mid-2030s one-third of all jobs could face the risk of being automated, according to a report from PwC. The sector of the workforce most likely to be disrupted will be those people who have low educational attainment.
Percentage of existing jobs at potential risk from automation. Image: Pwc
Anxiety over job losses caused by the increased use of machinery has been around for hundreds of years. With each new development, someone has faced the prospect of their livelihood or quality of life being changed irrevocably.
16th-century stockings
In the 16th century, all labour was manual labour. Until a clergyman named William Lee hit upon an idea to mechanize – at least in part – the production of stockings. He adapted looms that were used in the manufacture of rugs to make a long sheet of stocking material, which could then be cut and stitched into stockings. It was far quicker and cheaper than the traditional method.
There is a legend that Lee’s request for a patent on his machine was rejected by Queen Elizabeth I, who was concerned for the welfare of former stocking knitters, who would end up out of work.
At the time, his machine had limited wider impact but became the basis of other textile machine developments.
19th-century textile riots
Hundreds of years later, English textile workers faced bigger changes. And they weren’t the only ones.
As the Industrial Revolution gathered pace, people moved from rural communities into the new, fast-growing cities. There they found work in mills and factories, where steam-powered machines were driving unprecedented growth in output of items previously hand-crafted by artisan workers.
Farmworkers too faced the challenge of mechanization. Growing populations demanded more food, and that drove the adoption of machines to handle everything from sowing seeds to harvesting crops.
The reaction from working people was not uniformly positive. In the UK, a movement that became known as the Luddites struck back at the increased use of automation. They rioted, smashed machines and even set fire to business owners’ homes.
20th-century car manufacturing
The use of robots in vehicle manufacture became increasingly common in the latter part of the 20th century. Initially used to perform simple, repetitive tasks, they helped increase output, standardize production quality and keep costs under control.
In 1979, the Fiat motor company ran a TV ad showing the production of its Strada hatchback complete with the tagline “hand built by robots”.
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Assembly-line tasks such as welding and spray-painting were among the first jobs to migrate from people to robots. But humans were on hand to supervise the machines. As the technology has improved, the range of jobs passed on to robots has expanded to cover more complex procedures, such as fixing windscreens into vehicles. They are also widely used to move heavy and bulky items through factories.
Automation and the future
According to many estimates, there will be more jobs created over the next few years than lost by automation.
The challenge facing world leaders and policy-makers in the wake of COVID-19 will be to ensure that people aren’t overlooked in the rush to rebuild economies
“COVID-19 has accelerated our transition into the age of the Fourth Industrial Revolution,” says Klaus Schwab, founder and executive chairman of the World Economic Forum. “We have to make sure that the new technologies in the digital, biological and physical world remain human-centred and serve society as a whole, providing everyone with fair access.”
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| 2020-09-03T00:00:00 |
https://www.weforum.org/stories/2020/09/short-history-jobs-automation/
|
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|
AI Graphic Design: How Artificial Intelligence Can Help
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How Artificial Intelligence Can Help Graphic Design
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https://alltimedesign.com
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AI can be called a designer's best friend as it works with designers to increase their efficiency and speed, making their jobs more manageable.
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“Alexa, play Despacito.” Alexa, do this. Alexa, do that. Unless you’ve been hiding under a huge rock, you already know who Alexa is and what she can do. Look at how Artificial Intelligence (AI) plays an important part in our daily lives. Even if you don’t know it, AI is probably already a part of your life. From playing your favorite song and ordering pizza to fixing an appointment at the salon and using Google Assistant, AI has played an active role in making things quicker, faster, and ultimately better for human beings. If you’ve read enough articles or watched enough videos, you’ll know that AI has taken several industries by storm. With the need for speed, accuracy, and convenience greater than ever before, marketing and product teams are under pressure. They have to generate great brand stories at an accelerated pace if they want to attract customer attention. It is why AI and design go hand-in-glove. The purpose of AI is simple – it’s to make things simpler and better. And this is no different when it comes to the design industry. Let’s check how AI Graphic Design works with the help of custom AI software development companies.
Artificial Intelligence Graphic Design and Designer
Artificial Intelligence unlocks a whole new world of opportunities for designers. Before that, the hype, which surrounds it, must be cleared. Designers first have to make their minds on thinking that AI is either a guardian angel to help them work miracles or one of the four horsemen of the apocalypse who may take away their income-earning opportunities. It is good to think of AI as augmented intelligence. Simply put, it will help humans become better designers.
The common thought among people when it comes to AI is that it will replace their jobs. But that is not the scenario. AI is dependent on the human mind to understand the whys and the whats of their functions. While they can perform simple and complex tasks, without human intervention, AI has a long way to go before automating a designer’s role. You can also have a look at How to Work with a Graphic Designer
Designers initially had to involve in a lot of intricate manual work while being creative, but thanks to AI assisting them, they can get more done in a shorter frame of time. It’s just not AI even machine learning, virtual reality, mixed reality, and deep learning are leaving a mark on the field of design.
Usage of AI and Graphic Designing
Remember how we used to queue VCD shops during our childhood to get our cartoon movie cassettes? The shopkeeper gives you the best suggestions which you rent, watch, and return. Now the times have changed so much that we started binge-watching on online platforms. Online streaming platforms like Netflix and Amazon Prime now play the same shopkeeper’s role giving you suggestions of movies based on your watch list. This is how far AI has come.
AI can be called a designer’s best friend as it works with designers to increase their efficiency and speed, making their jobs more manageable. They assist them in carrying out complex tasks more efficiently in their hectic days. AI’s power will lie at the pace in which it can analyze vast amounts of data and offer suggestions on design adjustments. The designer has the liberty to cherry-pick and approve the adjustments based on that data.
A fast design prototyping can be done with an AI design tool where all the basic sketches are scanned in and a few parameters to be followed are entered. Then the cluster of the established UI components works to output a prototype alignment with the company’s design team. A well-established company like Airbnb is already generating design components with production-ready code from wired frame hand-drawn sketches using machine learning and computer vision-enabled AI.
One of the beneficial parts of AI for designers is that they are put with daily tedious tasks such as product localization and creating the same graphics in multiple languages. Companies like Netflix are using the intelligence system to translate the artwork personalization and localization of show banners into numerous languages. Once the system reads the master version, customized and localized graphics are instantly produced. The designer has to check the graphics, approve or reject, and manually tweak them. Imagine how much time can be saved!
The Perfect AI Design Brand Example
Another great example is Nutella. It used an AI algorithm to generate several millions of unique packaging designs. This AI algorithm was taken from a database of dozens of patterns and colors to curate seven million different visions of Nutella’s graphic identity, with all of them having a uniqueness. All seven million jars were sold in a month.
Grid launched in 2014 is an AI incorporated design that has produced many web pages according to their sites. Though these sites have mixed reviews, we are clear on what it holds for the future. This type of designing is still at its infancy, but we can expect a vast growth in the future
Adobe’s Sensei, similar to Grid, is at its beginning stage as well, but it has got far-reaching uses. As Adobe is continuing to develop Sensei, we are more likely to see tasks being managed more efficiently and faster than before.
AI offers designing tools that help designers create winning designs faster by automatically refining a product’s design based on the other successful ones. It provides entirely new design alternatives and would also report why these would improve the number of users.
In AI, we trust – with humans, we achieve
The future of AI has a lot more to offer for designers. Looking into the world of VR, AR, and MR, there are enough opportunities to showcase just how creative they can be. For instance, AI can help designers build 3D VR worlds instantly. Once a basic design is provided with parameters, AI creates million numerous models based on which the designer could choose.
For Alexa to play your favourite song, you have to tell her to do so. But as far as design is concerned, the software already comes inbuilt with the necessary intelligence to cognitively think on behalf of the designer. So, instead of seeing AI as a threat, designers should maximize their potential by blurring the line between machine and human creativity. Of course, there is only that much a machine can do. At the end of the day, it is up to the designer to bring their creative instincts to make sure the job is done in the way it is supposed to. But with AI by their side, this job will get a whole lot easier. For any further clarification on AI Designs, make your appointment with the top Graphic Designers at All Time Design.
Get in touch with us today to get your own design!
| 2020-09-17T00:00:00 |
2020/09/17
|
https://alltimedesign.com/ai-graphic-design/
|
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The Daily — Automation of job tasks may affect women more than men
|
The Daily — Automation of job tasks may affect women more than men
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https://www150.statcan.gc.ca
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[] |
A new Statistics Canada study is the first to examine in detail the risk of automation-related job transformation faced by women and men based on their job ...
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View the most recent version.
Information identified as archived is provided for reference, research or recordkeeping purposes. It is not subject to the Government of Canada Web Standards and has not been altered or updated since it was archived. Please " contact us " to request a format other than those available.
Recent advances in artificial intelligence have raised concerns about the potential impact of automation in the workplace. The COVID-19 pandemic may accelerate the implementation of new technology, as firms might look to make the production and delivery of goods and the provision of services more resilient in the future. While skilled workers may become more productive by complementing the tasks performed by the new technology or by working directly with it, others may need to upgrade their skills. In either case, jobs may be transformed, as robots and computer algorithms take over routine, non-cognitive duties, while humans specialize further in non-routine, cognitive tasks. These changes may affect women and men differently, depending on the tasks they perform and how automatable they are.
A new Statistics Canada study is the first to examine in detail the risk of automation-related job transformation faced by women and men based on their job tasks.
The study finds that in 2016 (prior to COVID-19), women and men were equally likely to face a high risk of automation-related job transformation (about 11%). However, women (44.4%) were more likely than men (34.8%) to face a moderate to high risk.
The gap in the proportion of women and men facing a moderate to high risk of automation-related job transformation could not be explained by gender differences in personal and work characteristics, such as age, education, industry and occupation. The gap may indicate that women and men perform different tasks that are not taken into account in the data. In fact, previous research has shown that women were more likely than men in the same occupation to report performing repetitive tasks, and this could put them at greater risk of automation-related job transformation.
The higher share of women facing a moderate to high risk of automation-related job transformation, compared with men, was also observed within many subgroups of the population. In certain cases, the gap was particularly large. For example, while 33.9% of men aged 55 or older faced a moderate to high risk, 58.6% of their female counterparts faced a similar risk. A larger share of women with no postsecondary qualifications faced a moderate to high risk than their male counterparts (75.8% and 60.0%, respectively).
Women who reported having a disability, who were not in a union or covered by a collective bargaining agreement, or who worked in a small firm (with 10 or fewer employees) were also more likely than their male counterparts to face a moderate to high risk of automation-related job transformation.
It is important to note that these risk estimates are largely based on the technological feasibility of automating job tasks. There are several reasons why employers may not immediately replace humans with robots, even if it is technologically feasible to do so. These reasons include financial, legal and demand-side factors, among others. For this reason, a high risk of automation does not necessarily imply a high risk of job loss. That being said, these results were estimated prior to COVID-19, which may accelerate automation in the workplace.
Note to readers The study uses the 2016 Longitudinal and International Study of Adults and draws on previous research that provides estimates of automation risk by occupation. Automation risk estimates are produced by various worker and firm characteristics and account for 25 different tasks that may vary within the same occupation, such as instructing, selling products or services, solving problems, or performing physical work. A high risk of automation-related job transformation is defined as a probability of 70% or more, while a moderate to high risk is defined as a probability of 50% or more.
Products
The study Automation and the Sexes: Is Job Transformation More Likely Among Women?, part of the Analytical Studies Branch Research Paper Series (Catalogue number11F0019M), is now available.
Contact information
For more information, contact us (toll-free 1-800-263-1136; 514-283-8300; [email protected]).
To enquire about the concepts, methods or data quality of this release, contact Marc Frenette, 613-864-0762; [email protected], Social Analysis and Modelling Division.
| 2024-09-20T00:00:00 |
2024/09/20
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https://www150.statcan.gc.ca/n1/daily-quotidien/200924/dq200924e-eng.htm
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[
{
"date": "2020/09/24",
"position": 96,
"query": "job automation statistics"
}
] |
Is Job Transformation More Likely Among Women?
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Automation and the Sexes: Is Job Transformation More Likely Among Women?
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https://www150.statcan.gc.ca
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[] |
The study finds that 44.4% of women in the paid workforce faced a moderate to high risk of job transformation as a result of automation (50% probability or ...
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Skip to text
Text begins
Acknowledgements
This study is funded by the Department for Women and Gender Equality.
Abstract
Recent advances in artificial intelligence and machine-learning technologies have fuelled fears of potential job losses among some workers. While the net impact of new technology on total jobs can be negative, positive or neutral, some workers may be more affected than others depending on how easily robots and algorithms can replace them, or how easily their skills complement the new technology. In the case of women and men, it is not clear who is likely to be most affected. While women are more likely to hold a university degree (typically associated with non-routine work that is more difficult to automate), they are also less likely to specialize in technology (which may limit their work opportunities in an increasingly digital world), but more likely to work in certain occupations that may be susceptible to automation ( e.g. , retail sales or clerical work). The objective of this study is to estimate the automation risks faced by women and men based on an existing methodology applied to Canadian data (the Longitudinal and International Study of Adults, Wave 3). The approach also uses expert consultations in the automatability of occupations, taking into account a wide range of tasks typically associated with those occupations (thus allowing automation risks to vary within occupations). The study finds that 44.4% of women in the paid workforce faced a moderate to high risk of job transformation as a result of automation (50% probability or above), compared with only 34.8% of men. Overall, the gap remains about the same when comparing women and men with similar characteristics, such as age, education, industry and occupation. However, several characteristics are associated with greater automation risks faced by women relative to men, including being aged 55 or older, having no postsecondary qualifications or postsecondary qualifications other than a degree, having low levels of literacy or numeracy proficiency, being born in Canada, having a disability, being a part-time worker, not being in a union or covered by a collective bargaining agreement, and being employed in a small to mid-sized firm.
Executive summary
Recent advances in artificial intelligence and machine-learning technologies have fuelled fears of potential job losses among some workers. Variations in the extent to which different types of workers may be at risk of job transformation as a result of automation technology may depend on how easily robots and algorithms can replace the tasks these workers perform in their jobs, or how easily their skills complement the new technology. While previous research tends to estimate the overall risk of automation for workers and occupational differences, less attention has been given to the degree to which automation technology will affect different groups of workers based on sociodemographic characteristics.
In the case of women and men, it is not clear who is likely to be most affected. While women are more likely to hold a university degree, which is typically associated with non-routine work that is more difficult to automate, they are also less likely to specialize in technology, which may limit their work opportunities in an increasingly digital world. However, women are also more likely to work in certain occupations that may be more susceptible to automation, such as retail sales or office support occupations.
This study estimates the risk of job transformation as a result of automation technology faced by women and men. An existing methodology is applied to Canadian data from the Longitudinal and International Study of Adults (LISA), Wave 3. This methodology uses expert consultations in the automatability of occupations and accounts for a range of tasks typically associated with those occupations. The automation risks were further adjusted by accounting for differences in 25 tasks that workers performed in their jobs ( e.g. , sharing information, selling product and services, advising people, performing physical work for a long period of time, using skill or accuracy with hands or fingers, reading directions and instructions, and using a programming language).
The study finds that 44.4% of women in the paid workforce faced a moderate to high risk of automation-related job transformation (50% probability or above), compared with only 34.8% of men. Overall, the gap remains about the same when comparing women and men with similar characteristics, such as age, education, industry and occupation.
Several characteristics are associated with greater automation risks faced by women relative to men, including being aged 55 or older, having no postsecondary qualifications or postsecondary qualifications other than a degree, having low levels of literacy or numeracy proficiency, having a disability, being a part-time worker, not being in a union or covered by a collective bargaining agreement, and being employed in a small to mid-sized firm. Differences in immigration status and the presence of preschool-aged or school-aged children were generally small between women and men.
Gender differences in the risk of job transformation as a result of automation technology may be attributable to different tasks performed within occupations that were not accounted for by the LISA task variables, such as repetitiveness of job tasks, or differences in the extent to which women and men engage in upskilling ( i.e. , learning new skills) while at work. Therefore, future research that examines more within-occupation differences between women’s and men’s job tasks and gender differences in upskilling behaviour would provide greater clarity on the differences in the risk of job transformation faced by women and men.
Introduction
In industrialized countries, the production of goods and services is highly dependent on technology and the degree to which activities or tasks are automated. Technological improvements have been a long-standing feature of advanced economies. Traditionally, such improvements have been gradual and have not been associated with major job disruptions. While some human jobs ( e.g. , bookkeeper) have been phased out to some extent as a result of technological improvements, others ( e.g. , Internet technology specialist) have been created to complement the new technology ( e.g. , Autor, Levy and Murnane 2003; Graetz and Michaels 2018).
More recently, advances in artificial intelligence and machine learning have led to technological developments in the production of goods and services traditionally in the domain of humans. For example, driverless vehicles have been employed in certain settings, as have robot writers. Robot diagnosticians have also been tested on humans. Although the commercial adoption of the new technologies takes time, their development has already led to concerns about the possibility of job disruption as the list of tasks that can be done only by humans grows smaller. Recent studies examining the risk of automation have concluded that only about 1 in 10 workers faces a high risk of automation-related job transformation in the coming years, while slightly more than one-quarter face a moderate risk ( e.g. , Frenette and Frank 2020). However, the adoption of new automation technology could accelerate as a result of the COVID-19 pandemic, as firms try to find ways to reduce the number of humans in the workplace.
Regardless of how quickly technological adoption unfolds, some workers may be more affected than others based on the degree of complementarity between their skills and the work that robots and computer algorithms can do. For example, women have become more likely to obtain a university degree than men in recent decades. This could favour women in the context of widespread automation, as highly educated workers may be better positioned to work alongside automated technology by focusing on more advanced processes not yet within the toolbox of robots, rather than performing physical or routine tasks. On the other hand, women with any postsecondary qualifications are less likely to select technology-related disciplines such as engineering, computer science or physics. This may limit their employment opportunities in an increasingly digital workforce. Women are also more likely to work in certain occupations associated with routine work, such as retail sales or office clerk positions.
For these reasons and many more, it is not clear a priori whether women face higher or lower automation-related risks than men, and the significance of any such difference. The purpose of this study is to provide insight on this issue by estimating the risks of automation-related job transformation faced by women and men. Furthermore, this study will attempt to explain the differences in the risks faced by women and men based on their observed individual and job characteristics. To do this, the study adopts an existing occupation and task-based approach to estimating automation‑related risks and applies it to Canadian data (the Longitudinal and International Study of Adults [LISA], Wave 3).
While several studies have examined the extent to which workers are at risk for job loss overall ( e.g. , Frey and Osborne 2013; Arntz, Gregory and Zierahn 2016), less attention has been given to potential gender differences (Hegewisch, Childers and Hartmann 2019; Peetz and Murray 2019).
It is important to examine the possibility of job transformation from a genderNote perspective for several reasons. First, the fact that men and women work largely in different occupations may result not only in different job loss risks, but also in different risks of job transformation for those who keep their jobs. For example, a female-dominated occupation in the health care field may be at a low risk for being eliminated because of automation, but might face substantial changes in the job tasks required if automation technology is introduced to assume some tasks (Hegewisch, Childers and Hartmann 2019). Therefore, knowledge of the implications of automation technology for both women and men can inform discussions on individual, institutional and public planning.
Moreover, job transformation resulting from technological advancements may increase workers’ need to upskill and retrain for changes in skill demand. This increased need for training may result in greater demand for certain types of postsecondary training and educational programs, as well as on-the-job training (Hegewisch, Childers and Hartmann 2019). Increased training needs will likely have particular implications for parents with respect to child care needs. This may affect women more if they are more highly impacted by automation technology than men, as women tend to take on a greater share of child care responsibilities within the household (Craig 2006; Guppy, Sakumoto and Wilkes 2019). Therefore, technology may create more demand for child care among affected women who seek retraining opportunities than among their male counterparts.
The next section reviews the related literature, followed by a description of the methods used and the main findings. This paper concludes by summarizing the key results and discussing useful next steps in research.
Literature review
Advances in automation technology have been at the forefront of recent discussions about the future of work. Generally, the literature focuses on the implications of this technology on workers’ jobs. Although much of the attention has focused on the extent to which jobs will be eliminated by automation technology ( e.g. , Frey and Osborne 2013), many researchers note that these technological developments will also create new job tasks and occupations ( e.g. , Acemoglu and Restrepo 2019; Hegewish, Childers and Hartmann 2019; Muro, Maxim and Whiton 2019). Some studies have employed a more nuanced task-based approach to argue that automation technology is more likely to change the tasks performed within most workers’ jobs, rather than replacing their jobs entirely ( e.g. , Acemoglu and Restrepo 2019; Acemoglu and Autor 2010; Autor, Levy and Murnane 2003).
Studies that apply the task-based approach have found that routine tasks are most likely to be replaced by automation technology, resulting in an increased demand for workers who can perform non-routine tasks that complement automated tasks (Acemoglu and Autor 2010). Furthermore, jobs that require more abstract and non-routine tasks, such as problem solving, persuasion or caring for others, may be less susceptible to automation than those that consist primarily of routine manual tasks that can be automated easily, such as bookkeeping, clerical work and repetitive tasks in production occupations (Acemoglu and Autor 2010; Muro, Maxim and Whiton 2019).
Studies that have examined how the potential for automation of tasks may affect men and women differently have produced varied results. Some research has found that women are at a higher risk of being affected than men ( e.g. , Roberts et al. 2019; World Economic Forum 2018). This disadvantage has been attributed to a variety of factors, such as women’s higher employment in part-time jobs, particularly in the service industry, and their underrepresentation in higher-paying jobs that are expected to expand in the future, such as occupations in programming and software development (Dellot 2018; Roberts et al. 2019; World Economic Forum 2018).
On the other hand, some research has found that differences in the skills that men and women use in their jobs may give women an advantage. For example, while a large proportion of men are employed in jobs that require specialized technical skills—often involving physical or manual tasks—women’s jobs tend to require more general and social skills (Madgavkar et al. 2019; RBC 2019). Therefore, while the jobs in which women are currently concentrated might be at a higher risk for automation ( e.g. , clerical and administrative occupations), women’s skills may be more transferable to emerging jobs in the digital economy (RBC 2019).
Research examining the relationship between automation and gender often points to occupational segregation between men and women as a driving factor in the differences in their risks of job transformation ( e.g. , Madgavkar et al. 2019). Occupational segregation by sex has resulted in many occupations being either male dominated ( e.g. , maintenance and equipment operation trades; industrial, electrical and construction trades) or female dominated ( e.g. , sales support occupations, professional occupations in health). This could result in differences in the extent to which men and women are susceptible to job loss as a result of automation technology. For example, researchers who employed the task-based approach found that, on average, women worked in jobs with a higher intensity of routine cognitive tasks than men, while men’s jobs were more likely to require routine manual tasks than women’s jobs.Note
Decreases were observed in the share of workers employed in both of these occupational task groups over time ( i.e. , occupations with a high intensity of routine cognitive tasks and routine manual tasks). This indicates that both men and women have been moving out of occupations with a high intensity of routine tasks and into both high-skilled and low-skilled occupations with a higher intensity of non-routine tasks (Acemoglu and Autor 2010; Autor, Levy and Murnane 2003; Levy and Murnane 2013; Spitz-Oener 2006).Note
However, despite similar trends observed across occupational task groups, some literature points to potential differences in how automation technology will affect women and men. Much of this literature points to the fact that automation primarily replaces tasks associated with physical and manual jobs, which tend to be in male-dominated occupations in fields such as production, transportation and construction (Muro, Maxim and Whiton 2019). Conversely, automation technology is less likely to be applied to interpersonal tasks ( e.g. , caregiving), which are largely associated with female-dominated occupations in fields such as health care, personal services and education (Muro, Maxim and Whiton 2019; Piasna and Drahokoupil 2017). In addition, because women are more highly educated than men on average, they may be at a lower risk of automation-related job transformation (Frenette and Frank 2020; Peetz and Murray 2019).
Others have found that women are more likely than men to work in both high-riskNote and low-riskNote occupations (Hegewisch, Childers and Hartmann 2019; Peetz and Murray 2019). This has led some to conclude that sex is not a major determinant of individuals’ susceptibility to job loss as a result of automation technology. Instead, a worker’s risk of job transformation can be determined primarily by the specific occupation in which they are employed (Peetz and Murray 2019).
Nevertheless, there is also evidence of gender differences within occupations. Piasna and Drahokoupil (2017) found that the tasks men and women performed within the same occupation differed. Across most occupational groups, women in the European Union were more likely than their male counterparts to report performing repetitive and routine tasks in their job, and were less likely to report performing complex tasks. These gender gaps were particularly pronounced for craft and trades workers, as well as for machine operators and assemblers. However, there were also notable gaps among managers, technicians and associate professionals. These results indicate that women may be more at risk for automation-related job transformation than men.
Technological advancements could positively affect women’s employment if they result in increased employment in female-dominated occupations (Goldin 1987). However, greater demand for workers could increase the status and wages and—subsequently—the desirability of these occupations. As a result, men who have lost their jobs because of automation technology may move into these occupations, displacing female workers (Peetz and Murray 2019).
Historically, the occupation of computer operator illustrates how a change in the perceived status of an occupation can result in it shifting from being a female-dominated occupation to being a male-dominated one. Because this occupation did not have a “gender precedent” when it was first introduced, it was associated with female-dominated clerical work because it involved the transfer of information (Light 1999). However, as the power of computers became more apparent over time, women were largely phased out of these jobs around the 1970s (Hicks 2017).
Previous research indicates that the changes in an occupation’s sex composition affect the wages and value associated with that occupation (Levanon, England and Allison 2009). This is illustrated by the change in computer-related occupations from female-dominated to male‑dominated, which resulted in an increase in pay and status for workers in these occupations (Hicks 2017; Peetz and Murray 2019). Generally, the higher the proportion of women in an occupation, the more the occupation is devalued (Levanon, England and Allison 2009). Therefore, if female-dominated occupations are less likely to be automated, increases in the number of men entering these occupations could raise their status, resulting in higher wages. However, if female-dominated occupations are at a higher risk of job transformation and there is growth in male-dominated occupations in the future, women may enter these occupations in higher numbers.
The shift in the sex composition of computer operators also has implications for women in the current context, as computer- and technology-related occupations are now identified as one of the occupations that are least at risk for automation-related job transformation. Furthermore, the skills associated with these jobs are in greater demand than social and interpersonal skills (Peetz and Murray 2019; Roberts et al. 2019).
Studies that estimate automation risks for women and men use different measures that produce varying results. For example, Madgavkar et al. (2019) examined the risk of job displacement as a result of automation technology across 10 countries, including Canada. Their methodology accounted for job loss by breaking occupations down into different activities (or job tasks), which were then categorized into broader groups of capabilities. They then ran a model that accounted for factors that affect the pace and extent of automation.Note Each occupation was given a percentage for automation adoption, with the assumption that an occupation is only automatable when all of its activities are automatable. Madgavkar et al. (2019) found that, overall, women were generally at a lower risk of job displacement than men, although the difference was small. However, there were variations across different countries, as each had a different rate of automation technology adoption. The Canadian estimates indicated that 24% of women’s jobs and 28% of men’s jobs were at risk for job displacement by automation technology.
Roberts et al. (2019) used an approach similar to the one used in this study to estimate the proportion of women and men in the United Kingdom who were employed in jobs with a high potential for automation. Specifically, they integrated the Frey and Osborne (2013) approach with the Arntz, Gregory and Zierahn (2016) approach through the use of data from the Programme for the International Assessment of Adult Competencies (PIAAC) and the United Kingdom’s Labour Force Survey. Roberts et al. (2019) found that women in the United Kingdom were twice as likely as men to be in the group identified as being at a high risk for potential automation (9% of women vs. 4% of men). Part of this difference was attributed to a higher likelihood of women to be employed in part-time jobs, which tend to have a higher potential for automation.
Methods
The methods used in this study are described thoroughly by Frenette and Frank (2020). Only a brief overview is provided below.
Frey and Osborne (2013) pioneered the literature on automation risks, and their work served as a starting point for the current study. They assigned a probability of facing automation over the next 10 or 20 years to the United States’ 2010 Standard Occupational Classification (SOC) codes. The assignment of risk was based on input from artificial intelligence experts who were presented with a list of job descriptions from 70 occupations in the Occupational Information Network (O*NET) and were asked “Can the tasks of this job be sufficiently specified, conditional on the availability of big data, to be performed by state of the art computer-controlled equipment?” This information was then used to model the probability of automation for all occupations in the 2010 SOC . The model also accounted for nine task variables that capture three “engineering bottlenecks to computerisation”: perception and manipulation, creativity, and social intelligence. This approach was critiqued by Arntz, Gregory and Zierahn (2016) on the grounds that it did not sufficiently distinguish occupations by job tasks, which is important because some occupations may comprise certain tasks that are fully automatable, while other components of the job may not be automatable at all. To account for this, Arntz, Gregory and Zierahn (2016) further adjusted Frey and Osborne’s (2013) probabilities based on 25 tasks, as well as various individual and workplace characteristics available in the 2012 PIAAC ( e.g. age, education, industry, and occupation). Therefore, these probabilities may vary not only by occupation, but also within occupations, to the extent that workers employed in the same occupation perform different observed tasks and possess different individual and workplace characteristics (which could lead to workers performing different unobserved tasks).
The current study more closely follows Arntz, Gregory and Zierahn (2016) by also adjusting for an extensive list of tasks.
First, the Frey and Osborne automation risk probabilities were assigned to workers in the 2016 LISA based on their occupation. Because the Frey and Osborne data are based on the 2010 SOC and LISA is based on the 2011 National Occupational Classification (NOC), a concordance file combining the two classification systems was used.Note
The automation risks assigned to each NOC code were then transferred to the 2016 LISA data file. These automation risks were further adjusted with the 25 task variables in LISA , which were virtually identical to the ones used by Arntz, Gregory and Zierahn (2016). Specifically, they include measures of cooperating or collaborating, sharing information, instructing, making speeches, selling products or services, advising people, planning and organizing own activities, planning and organizing activities of others, planning and organizing own time, persuading or influencing people, negotiating with people, solving problems of less than 5 minutes, solving problems of less than 30 minutes, performing physical work for a long period of time, using skill or accuracy with hands or fingers, reading directions or instructions, reading journals or scholarly publications, reading books, reading manuals or reference materials, writing articles for newspapers or newsletters, filling in forms, using advanced mathematics, using the Internet for work-related issues, using a programming language, and participating in real-time discussions on the Internet.Note
As noted by Frenette and Frank (2020), the automation risk probabilities were adjusted only for the 25 tasks and not by individual and workplace characteristics. Adjusting for tasks provides a conceptually clear measure of automation risk that is based solely on technological feasibility. Following these adjustments, the automation risks were estimated for women and men separately.Note The sample was limited to paid workers aged 18 and older with valid responses for all of the variables used in the analysis (described in the results section). This resulted in a sample of 2,267 workers.Note
Results
The automation risk index is determined entirely by the specific job held by the worker, which is defined as the occupation type and job tasks involved. As a result, gender differences in the risk of automation may reflect, in part, gender differences in occupations, which are shown in Chart 1.
Data table for Chart 1 Data table for Chart 1
Table summary
This table displays the results of Data table for Chart 1 Men and Women, calculated using percent units of measure (appearing as column headers). Men Women percent Management occupations 14.3 9.3 Business, finance and administration occupations 13.9 32.0 Natural and applied sciences and related occupations 19.0 5.0 Health occupations 2.4 8.9 Occupations in education, law and social, community and government services 7.9 19.0 Occupations in art, culture, recreation and sport 2.7 4.3 Sales and service occupations 18.3 18.6 Trades, transport and equipment operators and related occupations 13.8 1.5 Natural resources, agriculture and related production occupations 1.9 0.2 Occupations in manufacturing and utilities 5.8 1.2
In general, the occupational distributions of women and men were very different. In fact, it is easier to identify the one major occupational group with similar shares of both sexes—sales and service occupations. Men were more likely to hold jobs classified as trades, transport and equipment operators and related occupations (13.8% vs. 1.5%), as well as natural and applied sciences and related occupations (19.0% vs. 5.0%). In contrast, almost one-third (32.0%) of women worked in business, finance and administration occupations, compared with only 13.9% of men. Similarly, 19.0% of women were employed in occupations in education, law and social, community and government services, compared with only 7.9% of men.
Chart 2 shows the distribution of the probability of facing automation-related job transformation by sex. While women and men were equally likely to face a high risk of automation-related job transformation (typically denoted as 70% or above in the literature), women were far more likely to face a moderate risk (50% to 70%). More specifically, 33.9% of women faced a moderate risk, compared with only 24.0% of men. The chart also shows that women were considerably less likely than men to face a low risk of automation (below 50%)—only 55.6% of women occupied jobs at low risk of automation-related transformation, compared with 65.2% of men.
Data table for Chart 2 Data table for Chart 2
Table summary
This table displays the results of Data table for Chart 2. The information is grouped by Predicted risk of automation-related job transformation (appearing as row headers), Men and Women, calculated using percent units of measure (appearing as column headers). Predicted risk of automation-related job transformation Men Women percent Less than 10% 2.6 1.1 10% to less than 20% 13.3 8.6 20% to less than 30% 17.5 13.4 30% to less than 40% 17.9 17.2 40% to less than 50% 14.0 15.3 50% to less than 60% 15.6 19.6 60% to less than 70% 8.5 14.2 70% to less than 80% 8.3 7.9 80% to less than 90% 2.5 2.7 90% or more 0 0
Since the dividing line between women and men lies around the 50% risk cut-off, the remainder of this study will focus on the probability of facing a 50% or greater probability of automation-related job transformation. Overall, 44.4% of women were in this category, compared with only 34.8% of men (a difference of 9.7 percentage points).
Why were women more likely to face at least a moderate risk of automation than men? The answer is not clear a priori, as women and men had very different individual and workplace characteristics, as shown in Table 1. These characteristics could be related to the risk of automation. Consequently, a multivariate analysis will be done in an attempt to isolate the role of gender in the risk of automation. To provide some context, these differences in characteristics are described below.
Women were somewhat more likely to hold a degree than men. In particular, 25.5% of women and 21.1% of men held a bachelor’s degree. Women were also slightly more likely to hold a master’s degree than men (9.4% vs. 8.8%), but there were virtually no differences in the share of women and men holding a doctoral or first professional degree. Despite lower rates of degree holding among men, their mean literacy and numeracy scores were slightly higher than those of women.Note
It is important to note that the education and literacy and numeracy results are for the population of women and men aged 18 and older and employed in the paid workforce. In recent years, approximately three in five university students have been female. As more and more of these young women enter the workforce, the gap in educational attainment between women and men will likely increase further. Moreover, the gender gap in literacy and numeracy test scores may decline over time, as these measures are positively correlated with educational attainment.
Gender differences in immigration status and the presence of preschool-aged or school-aged children were generally quite small. However, women in the sample were less likely to report being married (62.0%) than men (66.0%).
More significant differences were observed for several other variables, including disability status. Women were almost twice as likely as men to report having a disability (18.4% vs. 9.9%).Note Women were also more than three times as likely as men to work part time (18.0% vs. 5.0%). However, about one-third (33.1%) of women in the paid workforce were in a union or covered by a collective bargaining agreement, compared with only about one-quarter (25.3%) of their male counterparts.
While women were slightly more likely to be employed in a small to medium-sized firm (50 employees or less), they were typically in very different industries than men. For example, men were about three times as likely as women to work in manufacturing (14.3% vs. 5.4%) or in mining, quarrying, and oil and gas extraction; utilities; and construction (9.9% vs. 3.1%). However, 32.2% of women were employed in educational services, and health care and social assistance, compared with only 9.0% of men. Women were also more likely than men to be employed in arts, entertainment and recreation, and accommodation and food services (6.9% vs. 2.8%).
These differences may have different impacts on the gender gap in automation risk. For example, Frenette and Frank (2020) found that workers with a postsecondary education typically faced a lower risk of automation. This fact should favour women, as they were more likely to hold a degree than men. However, women were also more likely to be employed part time, and Frenette and Frank (2020) found that part-time workers faced higher risks of automation.
To estimate the joint role of the gender differences reported in Table 1 on the gender differences in the probability of facing at least a moderate risk of automation, a multivariate framework was required. Table 2 presents the results of regressing a binary variable indicating a moderate to high risk of automation on a female indicator variable, as well as all of the variables from the analysis in Table 1. The key coefficient of interest is the one associated with the female variable, which indicates the gender difference in the probability of facing a moderate to high risk of automation after accounting for gender differences in all other independent variables, such as age and education.Note
The results indicate that, after accounting for the gender differences reported in the individual and workplace characteristics in Table 1, women still faced a considerably higher probability of a moderate to high automation risk. In fact, the difference was 11.4 percentage points (statistically significant at 0.1%), which was slightly larger than the unadjusted difference (9.7 percentage points). This means that the overall gender difference in automation risk cannot be explained by the observed gender differences in individual and workplace characteristics. Other factors may explain this gap, but these cannot be identified in the data.Note
However, certain groups of women faced a risk that was significantly higher than that of their male counterparts. Table 3 shows the percentage of women and men who faced a high risk of automation, by different socioeconomic group, both in the raw (unadjusted) data as well as after adjusting for the full set of covariates used in the model associated with Table 2 (through interaction terms). Although the unadjusted results could be of interest to many, they may be explained—to some extent—by differences in other socioeconomic characteristics. Therefore, the discussion will focus exclusively on the adjusted differences.
The gender differences in the probability of facing a moderate to high risk of automation were broad based. Statistically significant differences were registered for some categories of all characteristics examined.
For example, women aged 55 and older were 20.5 percentage points more likely to face a high risk of job transformation as a result of automation than their male counterparts. In contrast, women and men aged 18 to 24 faced about the same risk.
Other groups of women who faced a particularly high risk compared with their male counterparts include those who have no postsecondary qualifications (12.3 percentage points more likely than comparable men) or have a postsecondary education with no degree (13.1 percentage point difference), have a literacy or numeracy proficiency below Level 3 (11.6 and 16.8 percentage points, respectively), were born in Canada (12.9 percentage points), have a disability (18.2 percentage points), work part time (17.2 percentage points), are not in a union or covered by a collective bargaining agreement (13.7 percentage points), and are employed in small to mid-sized firms (with no more than 10 employees: 19.2 percentage points; or between 51 and 250 employees: 20.6 percentage points).Note
Conclusion
Fears of widespread job losses have arisen in response to recent, rapid advances in artificial intelligence. The COVID-19 pandemic could accelerate technological adoption, as firms try to find ways to reduce the number of humans in the workplace. While technological improvements may create considerable job opportunities for workers with complementary skills, some workers may be left behind. The objective of this study was to document and attempt to explain gender differences in the risk of job transformation as a result of automation.
This study found that 44.4% of women in the paid workforce faced a moderate to high risk of automation-related job transformation (50% probability or above), compared with only 34.8% of men. Overall, the gap remained about the same when comparing women and men with similar characteristics, such as age, education, industry and occupation. However, several characteristics were associated with greater automation risks faced by women relative to men, including being aged 55 or older, having no postsecondary qualifications or postsecondary qualifications other than a degree, having low levels of literacy or numeracy proficiency, being born in Canada, having a disability, being a part-time worker, not being in a union or covered by a collective bargaining agreement, and being employed in a small to mid-sized firm.
Some of the gender differences in automation risk may be attributable to different tasks performed within occupations, which were not accounted for by the LISA task variables. In particular, none of the task variables explicitly measured the extent to which individuals performed repetitive tasks in their jobs. Previous literature has shown that women were more likely to report performing repetitive tasks than men in the same occupation, which could put them at greater risk of automation-related job transformation (Piasna and Drahokoupil 2017).
Furthermore, gender differences in the extent to which workers engage in upskilling while at work have been found. Piasna and Drahokoupil (2017) found that women were less likely than men in the same occupation to upgrade their skills through on-the-job training, which could also contribute to women’s higher risk of job transformation. However, these differences were lower than within-occupation gender differences in the intensity of performing routine tasks, leading Piasna and Drahokoupil (2017) to conclude that women’s higher vulnerability to job transformation was largely driven by gender differences in the distribution of job tasks.
Therefore, the estimation of men’s and women’s risk of job transformation likely requires the consideration of additional factors. Future research might benefit from examining more within‑occupation differences in men’s and women’s job tasks, such as repetitive tasks, as well as differences in their training or upskilling behaviour. This may require new data, particularly more detailed task information.
Recent research has also examined gender differences in the perceptions of automation risk (Baird et al. 2018; Dodel and Mesch 2020). In Australia, similar proportions of men and women believed their job would not exist in 20 years because of automation (about one in five). However, men were more likely than women to be concerned about potentially losing their jobs to automation technology (Baird et al. 2018). Research from the United States suggests that there are no gender differences in how workers perceive the impact automation will have on their jobs (Dodel and Mesch 2020). In Canada, Loewen and Stevens (2019) also examined expectations and concerns regarding automation. However, results were not available by subgroup. Understanding whether expectations about automation align with one’s risk is important for personal and public planning purposes.
Tables
Table 1
Characteristics by sex
Table summary
This table displays the results of Characteristics by sex Men and Women, calculated using percent, mean and number units of measure (appearing as column headers). Men Women percent Age (years) 18 to 24 5.5 6.5 25 to 34 22.7 19.9 35 to 54 53.0 53.3 55 and older 18.9 20.3 Highest level of education completed Less than high school diploma 2.7 3.0 High school diploma 18.5 14.8 Trades certificate or apprenticeship 12.8 5.5 College certificate or diploma 25.7 27.9 University transfer program 0.1 0.3 University certificate below a bachelor's degree 4.5 4.1 Bachelor's degree 21.1 25.5 University certificate above a bachelor's degree 2.6 6.3 First professional degree 2.1 2.2 Master's degree 8.8 9.4 Doctoral degree 1.2 1.0 mean Literacy 296.9 289.8 Numeracy 296.2 275.6 percent Immigration status Born in Canada 78.8 79.9 Long-term immigrant (10 years or more) 14.8 12.6 Recent immigrant (less than 10 years) 6.4 7.4 Married 66.0 62.0 mean Number of preschool-aged children 0.2 0.2 Number of school-aged children 0.5 0.5 percent Have a disability 9.9 18.4 Work part time 5.0 18.0 In a union or covered by a collective bargaining agreement 25.3 33.1 Firm size (number of employees) 1 to 10 19.3 21.0 11 to 50 29.6 32.3 51 to 250 25.5 25.3 251 to 1,000 15.1 12.3 More than 1,000 10.5 9.1 Industry (one-digit NAICS 2012 code) Agriculture, forestry, fishing and hunting (1) 1.3 0.3 Mining, quarrying, and oil and gas extraction; utilities; and construction (2) 9.9 3.1 Manufacturing (3) 14.3 5.4 Wholesale trade, retail trade, and transportation and warehousing (4) 22.6 16.0 Information and cultural industries; finance and insurance; real estate and rental and leasing; professional, scientific and technical services; management of companies and enterprises; and administrative and support, waste management and remediation services (5) 25.7 21.7 Educational services, and health care and social assistance (6) 9.0 32.2 Arts, entertainment and recreation, and accommodation and food services (7) 2.8 6.9 Other services (except public administration) (8) 4.0 4.2 Public administration (9) 10.3 10.3 number Sample size 1,017 1,250
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| 2020-09-24T00:00:00 |
https://www150.statcan.gc.ca/n1/pub/11f0019m/11f0019m2020015-eng.htm
|
[
{
"date": "2020/09/24",
"position": 81,
"query": "job automation statistics"
},
{
"date": "2020/09/24",
"position": 93,
"query": "job automation statistics"
}
] |
|
Millions of Latinos at risk of job displacement by automation | UCLA
|
Millions of Latinos at risk of job displacement by automation
|
https://newsroom.ucla.edu
|
[
"Eliza Moreno"
] |
Some figures estimate that up to 70 percent of jobs in hospitality and 49 percent of construction jobs could soon become completely automated.
|
The potential acceleration of job automation spurred by COVID-19 will disproportionately affect Latinos in U.S. service sector jobs, according to a new UCLA report, which also urges state and local officials to start planning now to implement programs to support and retrain these workers.
The report, by the UCLA Latino Policy and Politics Initiative, looked at occupational data from the six states with the largest Latino populations and found an overrepresentation of Latinos in industries where jobs are more susceptible to automation, like construction, leisure and hospitality, agriculture, and wholesale or retail trade.
More than 7.1 million Latinos, representing almost 40% of the Latino workforce in those six states — Arizona, California, Florida, Illinois, New York and Texas — are at high risk of being displaced by automation, the report shows.
“As Latinos take a disproportionate financial hit from the COVID-19 crisis, now is a good time to focus on increasing training opportunities and to strengthen the social safety net to catch workers who are left behind,” said Rodrigo Dominguez-Villegas, the report’s author and director of research at the policy initiative.
“Millions of people have lost their jobs amid the pandemic, and the future of those jobs is uncertain as employers look to reduce costs by accelerating automation,” Dominguez-Villegas added. “For many Latinos, the economic recovery will not bring back their jobs.”
A failure to prepare Latinos for jobs in the digital economy and other growing sectors will come with economic repercussions to the U.S. by creating a shortage of skilled workers in an aging and shrinking labor force. As the nation’s youngest demographic group, with a median age of 30 years — compared with 44 for whites, 38 for Asians and 35 for African Americans — Latino workers can fill increasing workforce demands in health care and tech-focused jobs if enough resources are focused on retraining the Latino workforce, according to the report.
“In the face of COVID-19, global warming and economic chaos, Latinos are critical to America’s recovery,” said Sonja Diaz, founding director of the policy initiative. “Policymakers need to strengthen pathways to opportunity that are centered on workers of color or risk further financial ruin.”
With the exception of Florida, where Latinos are almost twice as likely to have a college degree and access to higher-skilled jobs, Latino workers, particulary those in California and Texas, could see heavy job loss in construction and hospitality from automation. Some figures estimate that up to 70 percent of jobs in hospitality and 49 percent of construction jobs could soon become completely automated.
The report makes the following policy recommendations to begin preparing the Latino workforce for a digitalized future:
Modernize unemployment insurance programs to expand eligibility and provide worker retraining assistance.
Create apprenticeship programs that provide career pathways for digitally oriented jobs and create a pipeline to employers.
Invest in broadband access and programs that connect Latinos with digital technologies.
Increase Latino enrollment in and graduation from higher education institutions and increase access to social-safety services such as housing, food and health care.
The report will be used as a baseline for discussion at a convening of policymakers, industry leaders, higher education administrators and training organizations organized by the Aspen Institute’s Latinos and Society Program in October 2020.
“Data is critical as policymakers work with the private sector to ensure that Latinos have access to the training and education opportunities necessary to drive our economy in the digital age,” said Domenika Lynch, executive director of the Aspen Institute program.
| 2020-09-30T00:00:00 |
https://newsroom.ucla.edu/releases/latinos-at-risk-of-job-displacement-by-automation
|
[
{
"date": "2020/09/30",
"position": 96,
"query": "automation job displacement"
}
] |
|
A Robot Tax to Fund Humans?: Analysing Future Prospects of ...
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A Robot Tax to Fund Humans?: Analysing Future Prospects of Universal Basic Income
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https://thepangean.com
|
[
"Mehul Goyal"
] |
A recent study put lawyers and Artificial Intelligence (AI) against each other. ... One general solution to this is Universal Basic Income (UBI).
|
“There would be fewer and fewer jobs that the robot cannot do better.” - Elon Musk
In the world that we are very quickly creating, we are going to see more and more things that look like science fiction and fewer things that look like jobs. A recent study put lawyers and Artificial Intelligence (AI) against each other. Their task was to review non-disclosure agreements. It took AI 26 seconds to finish a task with 94% accuracy. The same task took the human manifestation 92 minutes with 85% accuracy. Hedge funds managed by AI gave an average return of 8% for which the humans could barely manage a 2%. AI could see in X-rays what an experienced doctor could not and was also better at predicting the risk an underlying disease poses to the patient. From supermarket checkout staff to truck drivers, everything is on the verge of getting automated. Thanks to human ingenuity, we no longer interact with the staff at Blockbuster, rather we have Netflix doing the work for us. McKinsey has reported that whether it is repetitive low-skilled labour or high-skilled jobs, both are at risk of automation and as much as 30% of all jobs would be automated by 2030. The reality is that there is no clear limit to what AI can automate.
There’s no doubt that we are going to need lawyers and doctors in the future as there are some key things such as complex decision making and empathy that can’t be automated. Additionally, just because AI performs well in test settings doesn’t mean that it’s ready for the market just yet. We may need fewer humans to do the same amount of work and it’s not just AI we should be concerned about. The problem has gotten so bad that a new term has been coined- the ‘working poor’: people who earn below the poverty line despite actively working. What happens to these people when the basic entry-level jobs become obsolete in the face of reality?
Governments around the world need a narrative that helps people feel that they do belong and that they are being heard. One general solution to this is Universal Basic Income (UBI). That’s one of the ways through which governments can start rebuilding trust. What is it and what would the future of work look like with the implementation of UBI?
The Solution, how UBI can help?
Though UBI may be the most ambitious policy of our times, it’s gaining popularity and momentum around the world fueled by the coronavirus pandemic and a whole legacy of unemployment and poverty that it is leaving behind. The idea of guaranteeing every person with a minimum living wage income is appealing. Andrew Yang, one of the former 2020 Democratic Presidential candidates, has based most of his campaign around the UBI. According to him, “Technology is the oil of the 21st century”.
Nearly 200,000 people in South Korea's Gyeonggi province are a part of a radical experiment in UBI. They receive about $220 every three months. No questions asked. During the coronavirus pandemic, ‘Gyeonggi Pay’ was expanded to 13 million people in the province including new-borns to help people ride out the economic slowdown. There was one catch though, people had to spend the money only in their neighbourhoods to help stimulate the local economy.
The popularity of the local program during the pandemic has now drawn attention at the national level and there's another big reason. South Korea is one of the most automated countries in the world and about 15% of jobs in the country are projected to be automated by 2024. So some politicians, especially in the manufacturing hub of Gyeonggi, want to give all citizens about $430 every month to help prepare for a future with robots. The success of South Korea's experiment could change how other countries think about adopting their own UBI programs.
Right now people can’t decide what UBI is and what it should be. Though UBI in its raw sense is a fairly simple concept, its implementation has several ramifications. There are different schools of thought that pertain to evaluating UBI. While some see it in the setting where all other welfare schemes are scrapped and bureaucracy is eliminated, others demand UBI as an add-on to the existing welfare programs and want to keep UBI so high that work becomes optional.
But what if we hand out enough free money and people just spend it on booze and stop working? Well, that is not true. According to a 2013 World Bank study which specifically examined whether poor people waste their handouts on tobacco and alcohol if they receive it in the form of cash. The clear answer was, no they don't. The opposite is true. Other studies have shown that the richer you are, the more drugs and alcohol you consume. The lazy and drunk poor person is a stereotype rather than a reality.
A test run in Canada in the 1970’s showed that only 1% of the working population stopped working after UBI was rolled out and that too was attributed to child care reasons. On average, Canadians reduced their work time by less than 10% which was then dedicated to looking out for better jobs and going back to school. UBI aims to address that there are more motivations to work than to just cover up the necessities.
But if laziness and drugs are not a huge deal, why don’t our current welfare state programs solve poverty?
Many Strings Attached
The existing welfare schemes have loopholes somewhere. Welfare or unemployment programmes often come with a lot of strings attached, taking part in courses, applying to a certain number of jobs every month, and taking any kind of job offer no matter if it is a good fit or what it pays. Besides the loss of personal freedom, these conditions are often a huge waste of time and only served to make the unemployment statistics seem less bad. Often your time would be much better spent looking for the right job, continuing education or starting a business.
Another unwanted side effect of many welfare programmes is that they trap people in poverty and promote passive behaviour. Imagine a benefit of $1000 every month. In a lot of programs, if you earn a single dollar extra, the whole thing is taken away. If you take a job which is paying $1200, you might not only lose your benefits but because of your taxes and other costs, you might end up having lesser money than before. So if you actively try to improve your situation and your total income is not rising or actually shrinking, welfare can create a ceiling that traps people in poverty and rewards passive behaviour.
A basic income can never be cut and therefore getting a job and additional income would always make your financial situation better. Work is always rewarded. Instead of a ceiling, it creates a floor from which people can lift themselves, and unlike unemployment benefits that run out over time, these payments are a stable flow of income.
But even if UBI is the better model, is it economically feasible?
Where does it come from?
Although a Universal Basic Income contributes to the vitalisation of the consumer economy and increases small business revenue, it has its dark enemy: inflation. While demand props up and everything is rosy, prices inflate leaving everything just as it was before.
For the inflation problem and how to tackle it since the money is not being created by magic or printers, it needs to be transferred from somewhere. It is more of a shift of funds than the creation of new ones. Hence, no inflation. But how do we pay for it? There's no right answer here because the world is too diverse. How well-off the country is, what the local values are, are things like high taxes or cutting the defence budget politically acceptable or not? How much welfare state is already in place and is it effective? Each country has its own individual path to UBI. The easiest way to pay for UBI is to end all existing welfare and use the free funds to finance it. Not only would this make several government agencies disappear, which in itself saves money, it would also eliminate a lot of bureaucracy on the other hand.
On the other hand, cutting them could leave many people worse off than before. If the goal is to have a foundation for everybody there still needs to be a program of some sort because just like countries, all people are not the same.
The second way to finance UBI is by imposing higher taxes especially on the very wealthy and rich. The tax is to avenge the wealthy for reaping the most benefits of economic growth. The wealth gap is rapidly widening and many argue that it might be time to distribute the spoils more evenly to preserve social peace. Top percentiles get wealthier while the rest of the population stagnates. Inequality is increasingly getting skewed. There could be taxes on financial transactions, capital, land value, carbon or even robots.
So, UBI is not necessarily expensive. It can be balanced out with GDP growth as more spending stimulates the demand side of the economy. According to a recent study, a UBI of $1000 per month in the US could grow the GDP by 12% over eight years because it would enable poor people to spend more and increase overall demand. What about the people who do the dirty work? Who will work in the fields, crawl through sewers or lift bricks and stones? If you don't need to for survival, will people still do hard, boring and unfulfilling labour? UBI might give them enough leverage to demand better pay and working conditions.
A study calculated that every extra dollar going to wage earners would add about $1.21 to the national economy, while every extra dollar going to high-income Americans would add only 39 cents. Making poor citizens better off could be a smart economic tactic.
For those who demand enough UBI to cover all their costs and do no work, I have a problem. For me, the concept of work itself not being essential for survival is appalling. It would be unfair to portray work as just a chore. Work gives us something to do. It challenges us, it motivates us to improve, it forces us to engage. Many find friends or partners at work. We work for social status, wealth and for our place in the world. We're looking for something to do with our lives, and for many people work gives them meaning. Money alone wouldn't be enough to make people live a happy life. People need a differentiated and delineated purpose and simply giving people money without that purpose is a wrong solution.
Other concerns
Apart from this, there are other concerns associated with UBI too. If all welfare programs were exchanged for one single payment, this gives the government a lot of leverage. Individual programs are easier to attack or cut than a multitude. Populists might promise drastic changes to the UBI to get into power and a universal basic income does not tackle all problems when it comes to equality. For example, rents. While $1000 might be great in the countryside, it's not a lot for expensive metropolitan areas which could lead to poor people moving outwards and the difference between rich and poor becoming even more extreme.
Remember the catch? South Korean people had to spend locally. That they can’t spend it in a McDonalds? Telling people where they can spend money sounds like an efficient way to boost local consumption but this policy has a loophole. Money is fungible and that what people can spend locally means they're saving on whatever other money they have. That other money was earlier being spent on non-local or non-eligible types of goods and services.
There is one grave concern of governments using Universal Basic Income to practice political influence. The huge chunks of personal data collected about the preferences and choices of the consumer could lead to a massive data breach to achieve self-motivated interests. Although the South Korean government claims that it only analyses aggregate demand and not the individual data points, there’s still much obscurity. Governor Lee of the Gyeonggi province is using the success of Gyeonggi Pay as a pitch for the 2022 Presidential election and he's now leading in the polls. He plans to partly fund the UBI scheme through what he calls a ‘Robot Tax’, essentially a levy on factories that have automated their production.
Conclusion
So, is UBI a good idea? The honest answer is that we don't know yet. There needs to be a lot more research and bigger test runs. We need to think about what kind of UBI we want and what we're prepared to give up to pay for it. The potential is huge. It might be the most promising model to sustainably eliminate poverty. It might seriously reduce the amount of desperation in the world and make us all much less stressed out. There would still be very rich and poor people but we could eliminate fear, suffering and existential panic for a significant part of the population. A recent pilot program in Finland found that UBI didn't help in getting people who were unemployed into jobs but it did make them happier and less stressed. About 50% of participants reported being healthier and having better overall well-being while the rest remained the same.
Human nature has an inherent desire to be productive and to contribute something meaningful to the world. Sure, a lot of people do complain about their work and they dream of winning the lottery and never having to work again, perhaps these people may be happy for the first few months of not working but a few years down the road they may get the feeling of a life without purpose. When it comes to UBI, people often flippantly say that now everyone is free to pursue their passion but they forget the empathetic angle- everyone isn't them. There are also a lot many people in this world without passions or hobbies. It can be said that curiosity is the start of a passion. Follow the trail of what you're curious about and then develop that into a passion. Others still might even find that hard to do. So what happens to them? If technology does replace their jobs and they're unable to retrain, then what meaning will they find in life? That's personal to them and only they can solve that problem.
So automation is on the way. Advances in technology are too great to ignore. The people at the bottom of the workforce are going to start being replaced. UBI may be able to solve the issue of how everyone will be able to feed themselves in this future but what about that more burning question of purpose? Now that might be harder to solve.
| 2020-10-14T00:00:00 |
2020/10/14
|
https://thepangean.com/A-Robot-Tax-to-Fund-Humans-Analysing-Future-Prospects-of-Universal-Basic-Income
|
[
{
"date": "2020/10/14",
"position": 86,
"query": "universal basic income AI"
}
] |
Recession and Automation Changes Our Future of Work, But There ...
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Recession and Automation Changes Our Future of Work, But There are Jobs Coming, Report Says
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https://www.weforum.org
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[] |
The workforce is automating faster than expected, displacing 85 million jobs in next five years. The robot revolution will create 97 million new jobs.
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Amanda Russo, Public Engagement, World Economic Forum, +41 79 392 6898, [email protected]
Español I Français I Deutsch I عربي I Japanese
The workforce is automating faster than expected, displacing 85 million jobs in next five years
The robot revolution will create 97 million new jobs, but communities most at risk from disruption will need support from businesses and governments
In 2025, analytical thinking, creativity and flexibility are among the top skills needed; with data and artificial intelligence, content creation and cloud computing the top emerging professions
The most competitive businesses will be those that choose to reskill and upskill current employees
Geneva, Switzerland, 21 October 2020 – The Future of Jobs 2020 report has found that COVID-19 has caused the labour market to change faster than expected. The research released today by the World Economic Forum indicates that what used to be considered the “future of work” has already arrived.
By 2025, automation and a new division of labour between humans and machines will disrupt 85 million jobs globally in medium and large businesses across 15 industries and 26 economies. Roles in areas such as data entry, accounting and administrative support are decreasing in demand as automation and digitization in the workplace increases. More than 80% of business executives are accelerating plans to digitize work processes and deploy new technologies; and 50% of employers are expecting to accelerate the automation of some roles in their companies. In contrast to previous years, job creation is now slowing while job destruction is accelerating.
“COVID-19 has accelerated the arrival of the future of work,” said Saadia Zahidi, Manging Director, World Economic Forum. “Accelerating automation and the fallout from the COVID-19 recession has deepened existing inequalities across labour markets and reversed gains in employment made since the global financial crisis in 2007-2008. It’s a double disruption scenario that presents another hurdle for workers in this difficult time. The window of opportunity for proactive management of this change is closing fast. Businesses, governments and workers must plan to urgently work together to implement a new vision for the global workforce.”
Some 43% of businesses surveyed indicate that they are set to reduce their workforce due to technology integration, 41% plan to expand their use of contractors for task-specialized work, and 34% plan to expand their workforce due to technology integration.
By 2025, employers will divide work between human and machines equally. Roles that leverage human skills will rise in demand. Machines will be primarily focused on information and data processing, administrative tasks and routine manual jobs for white- and blue-collar positions.
New sense of urgency for the reskilling revolution
As the economy and job markets evolve, 97 million new roles will emerge across the care economy, in fourth industrial revolution technology industries like artificial intelligence, and in content creation fields. The tasks where humans are set to retain their comparative advantage include managing, advising, decision-making, reasoning, communicating and interacting. There will be a surge in demand for workers who can fill green economy jobs, roles at the forefront of the data and artificial intelligence economy, as well as new roles in engineering, cloud computing and product development.
For those workers set to remain in their roles in the next five years, nearly 50% will need reskilling for their core skills.
Despite the current economic downturn, most employers recognize the value of reskilling their workforce. An average of 66% of employers surveyed expect to see a return on investment in upskilling and reskilling of current employees within one year. They also expect to successfully redeploy 46% of workers within their own organization. “In the future, we will see the most competitive businesses are the ones that have invested heavily in their human capital – the skills and competencies of their employees,” Zahidi said.
Building a more inclusive future of work
The individuals and communities most negatively affected by the unprecedented changes brought about by COVID-19 are likely to be those that are already most disadvantaged. In the absence of proactive efforts, inequality is likely to be exacerbated by the dual impact of technology and the pandemic recession.
The Future of Jobs 2020 report partner ADP Research Institute tracked the impact of COVID-19 on the United States labour market. Between February and May 2020, data showed that displaced workers were, on average, mostly female, younger and had a lower wage. Comparing the impact of the global financial crisis of 2008 on individuals with lower education levels to the impact of the COVID-19 crisis, the impact today is far more significant and more likely to deepen existing inequalities.
"In the wake of COVID-19, the US workforce experienced immense change, and we were able to track this impact on the labour market in near real time," said Ahu Yildirmaz, Head of ADP Research Institute Labour Market Research. "While the swift and staggering job loss in the initial months was significant, it is only one anomaly of this 'recession.' Industry distribution, business size and worker demographics were all disrupted due to labour market changes brought about by COVID-19, signalling that this downturn is unlike any other in modern US history."
“The pandemic has disproportionately impacted millions of low-skilled workers,” said Jeff Maggioncalda, Chief Executive Officer of Coursera, another report partner. “The recovery must include a coordinated reskilling effort by institutions to provide accessible and job-relevant learning that individuals can take from anywhere in order to return to the workforce.”
Currently, only 21% of businesses worldwide are able to make use of public funds for reskilling and upskilling programmes. The public sector will need a three-tiered approach to help workers. This includes providing stronger safety nets for displaced workers, improving the education and training systems and creating incentives for investments in markets and the jobs of tomorrow.
Companies can measure and disclose their treatment of employees by adopting environmental, social and governance (ESG) metrics. This will help benchmark success, provide support where it is needed and ensure new gaps that arise are quickly identified and closed.
Remote working is here to stay but requires adaptation
Some 84% of employers are set to rapidly digitalize working processes, including a significant expansion of remote working. Employers say there is the potential to move 44% of their workforce to operate remotely.
According to the report, 78% of business leaders expect some negative impact on worker productivity. This suggests that some industries and companies are struggling to adapt quickly enough to the shift to remote working caused by the COVID-19 pandemic.
To address concerns about productivity and well-being, about one-third of all employers said they will take steps to create a sense of community, connection and belonging among their employees.
Career pivots become the “new normal”
The research also indicated that a growing number of people are making career changes to entirely new occupations. According to LinkedIn data gathered over the past five years, some 50% of career shifts into data and artificial intelligence are from different fields. That figure is much higher for sales roles (75%), content creation and production positions, such as social media managers and content writers (72%), and engineering roles (67%).
"As we think about ways to upskill or transition large populations of the workforce who are out of work as a result of COVID-19 into new, more future-proofed jobs, these new insights into career transitions and the skills required to make them have huge potential for leaders in the public and the private sector alike,” said Karin Kimbrough, Chief Economist at LinkedIn.
“Our research reveals the majority of transitions into jobs of tomorrow come from non-emerging jobs, proving that many of these jobs are more accessible than workers might think, Kimbrough continued. “If we can help individuals, and the leaders who are directing workforce funding and investment, identify the small clusters of skills that would have an outsized impact on opening up more sustainable career paths, we can make a real difference in addressing the unprecedented levels of unemployment that we're seeing globally.”
Data shows how long to reskill
According to The Future of Jobs Survey, core skills such as critical thinking, analysis and problem-solving are consistently top of the reskilling and upskilling priorities for educators and businesses. Newly emerging in 2020 are skills in self-management such as resilience, stress tolerance and flexibility.
Data from Coursera suggests that individuals could start gaining the top 10 skills for each emerging profession in people and culture, content writing, sales and marketing in one to two months. Those wishing to expand their skills in product development and data and artificial intelligence could do so in two to three months, and those switching into cloud computing and engineering could make headway in the new skillset through a four to five-month learning programme.
There has been a fourfold increase in the number of people seeking opportunities for online learning under their own initiative, a fivefold increase in employers offering their workers online learning opportunities and a ninefold enrolment increase in people accessing online learning through government programmes.
Those in employment are placing larger emphasis on personal development courses; those unemployed have placed greater emphasis on learning digital skills such as data analysis, computer science and information technology.
"The pandemic has accelerated many of the trends around the future of work, dramatically shrinking the window of opportunity to reskill and transition workers into future-fit jobs,” said Hamoon Ekhtiari, CEO of FutureFit AI. “No matter what prediction you believe about jobs and skills, what is bound to be true is heightened intensity and higher frequency of career transitions especially for those already most vulnerable and marginalized.”
“The Future of Jobs Report is a critical source of insights in supporting companies and government through these workforce transitions, and FutureFit AI is honoured to share our data and insights in the Report, Ekhtiari continued. “We look forward to continuing to contribute to a just, worker-first, and data-powered recovery as a partner of the World Economic Forum's New Economy & Society community and its Reskilling Revolutions Platform."
The Future of Jobs
Now in its third edition, The Future of Jobs report maps the jobs and skills of the future, tracking the pace of change. It aims to shed light on the pandemic-related disruptions in 2020, contextualized within a longer history of economic cycles and the expected outlook for technology adoption, jobs and skills in the next five years. The Future of Jobs survey informs the report. It is based on the projections of senior business leaders (typically Chief Human Resource Officers and Chief Strategy Officers) representing nearly 300 global companies, which collectively employ 8 million workers.
It presents the workforce planning and quantitative projections of chief human resource and strategy officers through to 2025, while also drawing on the expertise of a wide range of World Economic Forum executive and expert communities. The report features data from LinkedIn, Coursera, ADP and FutureFit.AI, which have provided innovative new metrics to shed light on one of the most important challenges of our time.
Notes to editors
Watch the Future of Jobs Report Video
Check out the Jobs Reset Summit liveblog and follow us on social #JobsReset
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| 2020-10-20T00:00:00 |
https://www.weforum.org/press/2020/10/recession-and-automation-changes-our-future-of-work-but-there-are-jobs-coming-report-says-52c5162fce/
|
[
{
"date": "2020/10/20",
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"date": "2020/10/20",
"position": 25,
"query": "job automation statistics"
},
{
"date": "2020/10/20",
"position": 26,
"query": "robotics job displacement"
},
{
"date": "2020/10/20",
"position": 25,
"query": "job automation statistics"
},
{
"date": "2020/10/20",
"position": 33,
"query": "robotics job displacement"
}
] |
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