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Ethical Considerations for AI Researchers

Use of artificial intelligence is growing and expanding into applications that impact people's lives. People trust their technology without really understanding it or its limitations. There is the potential for harm and we are already seeing examples of that in the world. AI researchers have an obligation to consider the impact of intelligent applications they work on. While the ethics of AI is not clear-cut, there are guidelines we can consider to minimize the harm we might introduce.


Introduction
A quick scan of recent papers covering the area of AI and ethics reveals researchers' admirable impulse to think about teaching intelligent agents human values (Abel, MacGlashan, and Littman 2016;Burton, Goldsmith, and Mattei 2016;Riedl and Harrison 2016). There is, however, another important and more immediate aspect of AI and ethics we ought to take into consideration. AI is being widely deployed for new applications; it's becoming pervasive; and it's having an effect on people's lives. AI researchers should reflect on their own personal responsibility with regard to the work they do. Many of us are motivated by the idea that we can contribute useful new technology that has a positive impact on the world. Positive outcomes have largely been the case with advanced technologies that improve cancer diagnosis and provide safety features in cars, for example. With vast amounts of computing power and a number of improved techniques, intelligent software is being adopted in more and more contexts that affect people's lives. How people use it is starting to matter, and the impact of our decisions matters.
Not surprisingly as the use of AI expands, negative consequences of its failures and design flaws are more visible. Much of the AI that has recently been deployed derives its intelligence from learning algorithms that are based on statistical analysis of data. The acquisition, applicability, and analysis of that data determine its output. Statistics shine when making predictions about distributions over populations. That predictive power fades when applied to individuals. There will be faulty predictions. The popular press is rife Copyright c 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. with misuses of statistical analysis and AI (Crawford 2016;O'Neil 2016). Given the growing use, the built-in uncertainties, and the public's tendency to blindly trust technology, we have a responsibility to consider the likely and unlikely outcomes of the choices we make when we are designing and developing tools or predictive systems to support decision making that affect people and communities of people.
Purposely malicious choices are obviously ethically unacceptable. In (Yampolskiy 2015), the author outlines various pathways that lead to dangerous artificial intelligence. Within the taxonomy, there are pathways that introduce danger into artificial intelligence 'on purpose.' The other pathways inadvertently lead to hazards in the system. You can decide for yourself if you are comfortable developing smart weapons, for example, and most of us would, at a minimum, pause to consider the implications of that decision. But the inadvertent pathways leading to dangerous AI can be difficult to foresee and may come about from subtle interactions. Our less obvious responsibility lies in giving careful consideration to our choices and being clear to ourselves and our stakeholders about assumptions, trade-offs, and choices we make.
Several other papers consider another ethical aspect in the fairness of automatic systems (O'Neil 2016;Hardt, Price, and Srebro 2016;NSTCCT 2016), and some even conclude that it's inherently impossible for most problems (Kleinberg, Mullainathan, and Raghavan 2016). One of the points I'll make is that discussions about fairness and societal impact can be cut off once an intelligent agent is introduced into the process. There is a popular feeling that machines don't make value judgments and are inherently unbiased. However, the assumptions we make when designing our systems are often based on subjective value judgments; for example, choosing data sets, selecting weighting schemes, balancing precision and recall. We have to be transparent about what we do and be clear about the choices we have made. The ultimate purpose matters and the decisions you come to must be communicated.

Blind Trust in Technology
Although there are pockets of skepticism towards intelligent systems, by and large people are content to offload decisions to technology. In May 2016, there was a widely publicized crash involving a Tesla Motors car being driven in computer-assisted mode. It appears the driver had undue faith in the capabilities of the car (Habib 2017). The following week another driver following a GPS unit steered her car into Ontario's Georgian Bay (MiQuigge 2016). These extreme examples reveal a trend in the general population to trust the smart devices in our lives.
Ideally government agencies and jurisdictions would apply the principles of open government and transparency when contracting with suppliers for decision-making tools. In practice that hasn't been the case. Last year, two researchers filed 42 open records requests in 23 different states asking for information about software with predictive algorithms used by governments as decision support tools (Brauneis and Goodman 2017). Their goal was to understand the policies built into the algorithms in order to evaluate their usefulness and fairness. Only one of the jurisdictions was able to provide information about the algorithms the software used and how it was developed. Some of those who did not respond cited agreements with vendors preventing them from revealing information, but many did not seem concerned about transparency in their process nor the need to understand the technology. Assuming the best intentions of the decision makers, they are also demonstrating great faith in the technology and vendors they contract with.
There is also evidence that users of these systems, judges and hiring managers for example, weight AI guidance too heavily. Without tools, when people are making decisions, there is public awareness that decisions are made within some context. We understand that individuals can be influenced even subconsciously by their biases and prejudices. Technologically assisted decisions tend to shut down the conversation about fairness despite their having a large effect on people's lives. Those affected may not have the opportunity to contest the decisions. If important decisions are made through our models, we must use care in developing them and clearly communicate the assumptions we make.

Ethical Obligations
Physicians and attorneys have well-established codes of ethics. Doctors famously commit to not doing any harm. Implied in that concept is the idea that there is potential to do harm. It is clear from many examples, some of which I mention in this paper, that there is the potential for harm in our work, and given people's lack of understanding of the limits of and the trust they place in technology, AI researchers have a personal, ethical obligation to reflect on the decisions we make.
Ethical thinking helps us to make choices and just as importantly provides a framework to reason about those choices. The framework we use (explicitly or not) is defined by a set of principles that guide and support our decisions. One of the difficult things about defining ethical standards is deciding the values to base them on. Ethics issues will undoubtedly be discussed and argued within the community and the world generally in the coming years. Each of us can start by considering our own roles and being consciously aware of the effects our work can have.
The stakeholders who decide to deploy intelligent decision making, government agencies for example, generally aren't qualified to assess the assumptions, models and algorithms in it. This asymmetrical relationship puts the burden on those with the information to be clear, honest, and forthcoming with it. Those at a disadvantage depend on us to inform them about technology's fitness for their purpose, its reliability and accuracy. We usually focus on the technical aspects of our work like selecting highly predictive models and minimizing error functions, but when applying algorithmic decision-making that will affect human beings, we have a responsibility to think about more.

Recommendations for Consideration
Ethics is not science. But it is possible to ground our thinking in well-defined guidelines to assist in making ethical decisions for AI development. A formal framework may even emerge within the researcher community with time. In the short-term, the following is a list of thoughts and questions to ask ourselves when designing predictive or decisionmaking systems.

Relevance of data and models
It is important to think carefully about the data used to train our technology. Are the data and models appropriate to the real-life problem they are solving? It is tempting to believe causal forces are at play when we find correlation on a single dataset. Does the data capture the true variable of interest? Is it consistent across observations and over time? We often introduce a proxy variable because the variable we need isn't available or isn't easy to quantify. Can your findings be calibrated against the real-world situation? Even better could you measure the actual outcome you're trying to achieve?
In 2008, Researchers at Google had the idea that an increase in search queries related to the flu and flu symptoms could be indicative of a spreading virus. They created the Google Flu Trends (GFT) web service to track Google users' search queries related to the flu. If they detected increased transmission before the numbers from the U.S. Centers for Disease Control and Prevention (CDC) came out, earlier interventions could reduce the impact of the virus. The initial article reported 97% accuracy using the CDC data as the gold standard (Ginsberg et al. 2009). However, a follow-up report showed that in subsequent flu seasons GFT predicted more than double what the CDC data showed (Lazer et al. 2014). Given the first year's high accuracy, it would have been easy for the researchers to believe they had discovered a strong, predictive signal. But online behavior isn't necessarily a reflection of the real world. There are several factors that might make the GFT data wrong. One of them is that the underlying algorithms of Google Search itself (the GFT researchers don't control those) can change from one year to the next. Also users' search behavior could have changed. Mainly, however, people's search patterns are probably not a good single indicator of a spreading virus. There are many other factors and various reasons people might search for information.
Training data rarely aligns with real-life goals. In (Lipton 2016), Lipton presents challenges to providing and even defining interpretability of machine learning outputs. He identifies several possible points of divergence between training data and real-life situations. For example, off-line training data is not always representative of the true environment, and real-world objectives can be difficult to encode as simple value functions. Often we work with data that was collected for other purposes and almost never under ideal, controlled circumstances. What was the original purpose in collecting the data, and how did that determine its content? In July of 2015, another group at Google had to apologize for its Photos application identifying a black couple as gorillas (Guynn 2015). Their training dataset was not representative of the population it was meant to predict. Also, there are limits to the amount of generalization we can expect from any learning method trained on a particular dataset.
Is it possible your dataset contains biases? When making decisions related to hiring, judicial proceedings, and job performance, for example, many personal characteristics are legally excluded. Also, humans are good at discarding variables they recognize as irrelevant to the decision to be made; computers are blind to those considerations. Are there other characteristics that are closely correlated with legally and ethically protected ones? If you don't consider those, you can inadvertently treat people unfairly based on protected or irrelevant characteristics. There is often a trade-off between accuracy and the intelligibility of a model (Caruana et al. 2015). More predictive but harder-tounderstand models can make it difficult to know which personal characteristics determine the decision and are therefore not available for validation against human judgment.
In (Caruana et al. 2015) the authors describe a system that learned a rule that patients with a history of asthma have a lower risk of dying from pneumonia. Based on the data used to train the system, their model was absolutely correct. However, in reality asthma sufferers (without treatment) have a higher risk of dying from pneumonia. Because of the increased risk, when patients with a history of asthma go to the hospital, the general practice is to place them in an intensive care unit. The extra attention they receive decreases their risk of dying from pneumonia even below that of the general population. It is our natural inclination to develop models with the highest accuracy. However, the necessity of visibility into decisions where people's lives are concerned, may increase the importance of explainability at the expense of some predictive performance. In all cases, our stakeholders must understand the decisions we make and the tradeoffs implied by them.

Safeguards for Failures and Misuse
Even experienced researchers with the best intentions are inclined to favor the positive outcomes of their work. We highlight positive results, but we should also think through failure modes and possible unintended consequences. What about misuse? There isn't a lot you can do about a person determined to use the technology in ways it wasn't intended, but are there ways a good-faith user might go wrong? Can you add protections for that?
The 2016 Tesla accident mentioned before was catastrophic. The driver used computer-assisted mode in condi-tions it was expressly not designed for resulting in his death. The accident was investigated by two government agencies. The first finding from the National Highway Traffic and Safety Administration found that the driver-assist software had no safety defects and declared that, in general, the vehicles performed as designed (Habib 2017) implying that responsibility for use of the system falls on the operator. A later investigation from The National Transportation and Safety Board found otherwise (NTSB 2016). They declared that the automatic controls played a major role in the crash. The fact that the driver was able to use computer assistance in a situation it was not intended for was problematic. The combination of human error and insufficient safeguards resulted in an accident that should not have happened.

Accuracy
How accurate is your algorithm and how accurate does it need to be? Do your stakeholders understand the number of people who will be subject to a missed prediction given your measure of accuracy? A model that misses only 1% shows phenomenally good performance, but if hundreds or thousands of people are still adversely affected, that might not be acceptable. Are there human inputs that can compensate for the system's misses and can you design for that? What about post-deployment accuracy? Accuracy in training data doesn't always reflect real usage. Do you have a way to measure runtime accuracy? The world is dynamic and changes with time. Is there a way to continue to assess the accuracy after release? How often does it have to be reviewed?

Size and severity of impact
Think about the numbers of people affected. Of course, you want to avoid harming anyone but knowing the size or the severity of negative consequences can justify the cost of extra scrutiny. You might also be able to design methods that mitigate for them. Given an understanding of the impact, you can make better decisions about the value required by the extra effort.

Conclusion
Individual researchers, especially in commercial operations, don't always have the chance to communicate clearly and transparently with clients. At least being transparent with your immediate stakeholders can set the right expectations for them when they represent your work down the line. You are necessarily making decisions about the models and software you develop. If you don't surface those decisions to discuss their effect, they may never be brought to light.
A short paper cannot cover such a large and multi-faceted issue. The main idea is for each of us to think individually about our own responsibilities and the impact our work can have on real lives. It's useful to spend time thinking about our assumptions and the trade-offs we make in the context of the people who will be affected. Communicating those to everyone concerned is also critical. Modern versions of the Hippocratic Oath are still used by many medical schools. The spirit of the oath is applicable to most research affecting human beings. One phrase is especially general and worth keeping in mind: