Artificial Intelligence

Ireland AI Strategy (2021)

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  • Building public trust in AI
    • Strand 1: AI and society
    • Strand 2: A governance ecosystem that promotes trustworthy AI
  • Leveraging AI for economic and societal benefit
    • Strand 3: Driving adoption of AI in Irish enterprise
    • Strand 4: AI serving the public
  • Enablers for AI
    • Strand 5: A strong AI innovation ecosystem
    • Strand 6: AI education, skills and talent
    • Strand 7: A supportive and secure infrastructure for AI
    • Strand 8: Implementing the Strategy


Check out the full document here.

Australia New AI Regulations Framework

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Over the past few weeks/months we have seen more and more countries addressing the potential issues and challenges with Artificial Intelligence (and it’s components of Statistical Analysis, Machine Learning, Deep Learning, etc). Each country has either adopted into law controls on how these new technologies can be used and where they can be used. Many of these legal frameworks have implications beyond their geographic boundaries. This makes working with such technology and ever increasing and very difficult challenging.

In this post, I’ll have look at the new AI Regulations Framework recently published in Australia.

[I’ve written posts on what other countries had done. Make sure to check those out]

The Australia AI Regulations Framework is available from, is a 240 page report giving 38 different recommendations. This framework does not present any new laws, but provides a set of recommendations for the government to address and enact new legislation.

It should be noted that a large part of this framework is focused on Accessible Technology. It is great to see such recommendations.  Apart from the section relating to Accessibility, the report contains 2 main sections addressing the use of Artificial Intelligence (AI) and how to support the implementation and regulation of any new laws with the appointment of an AI Safety Commissioner.

Focusing on the section on the use of Artificial Intelligence, the following is a summary of the 20 recommendations:

Chapter 5 – Legal Accountability for Government use of AI

Introduce legislation to require that a human rights impact assessment (HRIA) be undertaken before any department or agency uses an AI-informed decision-making system to make administrative decisions. When an AI decision is made measures are needed to improve transparency, including notification of the use of AI and strengthening a right to reasons or an explanation for AI-informed administrative decisions, and an independent review for all AI-informed administrative decisions.

Chapter 6 – Legal Accountability for Private use of AI

In a similar manner to governmental use of AI, human rights and accountability are also important when corporations and other non-government entities use AI to make decisions. Corporations and other non-government bodies are encouraged to undertake HRIAs before using AI-informed decision-making systems and individuals be notified about the use of AI-informed decisions affecting them.

Chapter 7 – Encouraging Better AI Informed Decision Making

Complement self-regulation with  legal regulation to create better AI-informed decision-making systems with standards and certification for the use of AI in decision making, creating ‘regulatory sandboxes’ that allow for experimentation and innovation, and rules for government procurement of decision-making tools and systems.

Chapter 8 – AI, Equality and Non-Discrimination (Bias)

Bias occurs when AI decision making produces outputs that result in unfairness or discrimination. Examples of AI bias has arisen in in the criminal justice system, advertising, recruitment, healthcare, policing and elsewhere. The recommendation is to provide guidance for government and non-government bodies in complying with anti-discrimination law in the context of AI-informed decision making

Chapter 9 – Biometric Surveillance, Facial Recognition and Privacy

There is lot of concern around the use of biometric technology, especially Facial Recognition. The recommendations include law reform to provide better human rights and privacy protection regarding the development and use of these technologies through regulation facial and biometric technology (Recommendations 19, 21), and a moratorium on the use of biometric technologies in high-risk decision making until such protections are in place (Recommendation 20).

In addition to the recommendations on the use of AI technologies, the framework also recommends the establishment of a AI Safety Commissioner to support the ongoing efforts with building capacity and implementation of regulations, to monitor and investigate use of AI, and support the government and private sector with complying with laws and ethical requirements with the use of AI.

Regulating AI around the World

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New Zealand


  • Inclusive national conversation on the use of AI
  • Mitigation of risks associated with AI applications



  • Internal Governance Structures and Measures
  • Operations Management, minimizing bias, explainability and robustness
  • Stakeholder Interaction and Communication.



      • Principles for the Use of AI in Government
      • Common Policy form Implementing Principles
      • Catalogue of Agency Use Cases of AI
      • Enhanced AI Implementation Expertise

USA – Washington State






Truth, Fairness & Equality in AI – US Federal Trade Commission

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Deep Learning: The Latest Trend In AI And ML | Qubole











Responsible AI: Principles & Standards around the World

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Standard/Principles Description

Demographics vs Psychographics for Machine Learning

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When preparing data for data science, data mining or machine learning projects you will create a data set that describes the various characteristics of the subject or case record. Each attribute will contain some descriptive information about the subject and is related to the target variable in some way.

In addition to these attributes, the data set will be enriched with various other internal/external data to complete the data set.

Some of the attributes in the data set can be grouped under the heading of Demographics. Demographic data contains attributes that explain or describe the person or event each case record is focused on. For example, if the subject of the case record is based on Customer data, this is the “Who” the demographic data (and features/attributes) will be about. Examples of demographic data include:

  • Age range
  • Marital status
  • Number of children
  • Household income
  • Occupation
  • Educational level

These features/attributes are typically readily available within your data sources and if they aren’t then these name be available from a purchased data set.

Additional feature engineering methods are used to generate new features/attributes that express meaning is different ways. This can be done by combining features in different ways, binning, dimensionality reduction, discretization, various data transformations, etc. The list can go on.

The aim of all of this is to enrich the data set to include more descriptive data about the subject. This enriched data set will then be used by the machine learning algorithms to find the hidden patterns in the data. The richer and descriptive the data set is the greater the likelihood of the algorithms in detecting the various relationships between the features and their values. These relationships will then be included in the created/generated model.

Another approach to consider when creating and enriching your data set is move beyond the descriptive features typically associated with Demographic data, to include Pyschographic data.

Psychographic data is a variation on demographic data where the feature are about describing the habits of the subject or customer.  Demographics focus on the “who” while psycographics focus on the “why”. For example, a common problem with data sets is that they describe subjects/people who have things in common. In such scenarios we want to understand them at a deeper level. Psycographics allows us to do this. Examples of Psycographics include:

  • Lifestyle activities
  • Evening activities
  • Purchasing interests – quality over economy,  how environmentally concerned are you
  • How happy are you with work, family, etc
  • Social activities and changes in these
  • What attitudes you have for certain topic areas
  • What are your principles and beliefs

The above gives a far deeper insight into the subject/person and helps to differentiate each subject/person from each other, when there is a high similarity between all subjects in the data set. For example, demographic information might tell you something about a person’s age, but psychographic information will tell you that the person is just starting a family and is in the market for baby products.

I’ll close with this. Consider the various types of data gathering that companies like Google, Facebook, etc perform. They gather lots of different types of data about individuals. This allows them to build up a complete and extensive profile of all activities for individuals. They can use this to deliver more accurate marketing and advertising. For example, Google gathers data about what places to visit throughout a data, they gather all your search results, and lots of other activities. They can do a lot with this data. but now they own Fitbit. Think about what they can do with that data and particularly when combined with all the other data they have about you. What if they had access to your medical records too!  Go Google this ! You will find articles about them now having access to your health records. Again combine all of the data from these different data sources. How valuable is that data?


Ethics in the AI, Machine Learning, Data Science, etc Era

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Ethics is one of those topics that everyone has a slightly different definition or view of what it means. The Oxford English dictionary defines ethics as, ‘Moral principles that govern a person’s behaviour or the conducting of an activity‘.

As you can imagine this topic can be difficult to discuss and has many, many different aspects.

In the era of AI, Machine Learning, Data Science, etc the topic of Ethics is finally becoming an important topic. Again there are many perspective on this. I’m not going to get into these in this blog post, because if I did I could end up writing a PhD dissertation on it. 

But if you do work in the area of AI, Machine Learning, Data Science, etc you do need to think about the ethical aspects of what you do. For most people, you will be working on topics where ethics doesn’t really apply. For example, examining log data, looking for trends, etc

But when you start working of projects examining individuals and their behaviours then you do need to examine the ethical aspects of such work. Everyday we experience adverts, web sites, marketing, etc that has used AI, Machine Learning and Data Science to delivery certain product offerings to us.


One particular area that I will not work on is Location Based Advertising. Imagine walking down a typical high street with lots and lots of retail stores. Your phone vibrates and on the screen there is a message. The message is a special offer or promotion for one of the shops a short distance ahead of you. You are being analysed. Your previous buying patterns and behaviours are being analysed, Your location and direction of travel is being analysed. Some one, or many AI applications are watching you. This is not anything new and there are lots of examples of this from around the world.

But what if this kind of Location Based Advertising was taken to another level. What if the shops had cameras that monitored the people walking up and down the street. What if those cameras were analysing you, analysing what clothes you are wearing, analysing the brands you are wearing, analysing what accessories you have, analysing your body language, etc. They are trying to analyse if you are the kind of person they want to sell to. They then have staff who will come up to you, as you are walking down the street, and will have customised personalised special offers on products in their store, just for you.

See the segment between 2:00 and 4:00 in this video.  This gives you an idea of what is possible.

Are you Ok with this?

As an AI, Machine Learning, Data Science professional, are you Ok with this?

The technology exists to make this kind of Location Based Marketing possible. This will be an increasing ethical consideration over the coming years for those who work in the area of AI, Machine Learning, Data Science, etc

Just because we can, doesn’t mean we should!