Ethics for artificial intelligence, machine learning and automated decision making
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Machine Learning and Artificial Intelligence are having an ever increasing impact on our lives. This presentation discusses some of the ethical issues associated with automated decision making systems, driven by machine learning and predictive models. It also provides a risk assessment framework for assessing the potential risks of such systems.
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Ethics for artificial intelligence, machine learning and automated decision making
- 1. Ethical Perspectives on Personal Data, Machine Learning and Automated Decision Making Dr Steven Finlay steve.finlay@virginmedia.com
- 2. Objectives • Discuss some of the ethical issues associated with personal data, machine learning and automated decision making. • Present a general and pragmatic framework for assessing the risk associated with using new types of personal data, and new applications of predictive models. 2
- 3. 3 Agenda 1. Introduction 2. A bit about ethics 3. Ethics and personal data 4. Ethics, machine learning and automated decision making. A risk management framework
- 4. Introduction • Why consider ethical issues in automated decision making? – Automated decision making, using personal data and based on predictive models (e.g. credit scoring and direct marketing models) is old hat to those of us working in financial services. – In widespread use since 1960s. – Lots of existing laws and regulations. – It’s data driven and unbiased, right? 4
- 5. Introduction • Recent explosion in Machine Learning/Predictive Analytics based systems, which are replacing or supporting human decision making in many walks of life • Siegal (2016) lists well over 100 uses for predictive models. • All automated decision making systems display bias! – The question is: Is it unfair, unethical or illegal bias? • E.g. when did you last assess the gender, race, religion or sexual bias expressed by your credit scoring systems? • On-going concerns being raised by governments, regulators and the media over the data that organisations hold, and the uses to which it is put. 5
- 6. 6 Agenda 1. Introduction 2. A bit about ethics 3. Ethics and personal data 4. Ethics, machine learning and automated decision making. A risk management framework
- 7. 1. Ethics, sometimes known as philosophical ethics, ethical theory, moral theory, and moral philosophy, is a branch of philosophy that involves systematizing, defending and recommending concepts of right and wrong conduct, often addressing disputes of moral diversity. The term comes from the Greek word ἠθικός ethikos from ἦθος ethos, which means "custom, habit". The superfield within philosophy known as axiology includes both ethics and aesthetics and is unified by each sub-branch's concern with value… http://en.wikipedia.org/wiki/Ethics Alternatively 2. It’s about right and wrong. Ethics is…. Subjective, personal, unique… 7 A bit about ethics. Definitions
- 8. Common ethical frameworks 8 Consequentialist “The means justify the ends” Non-Consequentialist “It’s more about the journey than where you end up…” Virtues “Virtuous modes of behaviour” (Aristotle) (Human) rights “Right to life, liberty, property, privacy, etc.” (Locke and Rawls) Religious Teaching (e.g. the ten commandments) Kant’s ethical theory Universality: Ethical is something all rational people would agree with Golden rule “Do unto others as you would have done unto you” (Do no evil) Utilitarianism “Greatest good for the greatest number” (Jeremy Bentham and John Stuart Mills) Ethics
- 9. Ethics in practice • All ethical frameworks have their weaknesses… 9
- 10. A bit about ethics. Relevance in the real world… • If I follow all laws and regulations, then that’s all I need to worry about right? • Lots of laws allow unethical actions to occur: “It is illegal to give alcohol to a child under 5” Another example is tax avoidance: A great example of what we mean when we talk about the spirit of the law as opposed to the letter of the law 10 Legal Ethical
- 11. A bit about ethics. Relevance in the real world… • It pays to be ethically minded: • Organizations adopting ethical policies tend to reap the benefits. • Largest ever study of the relationship between ethical performance and financial performance: – Losses from reputational damage, resulting from actions that are perceived to be unethical, are particularly severe. – “Corporate virtue in the form of social and, to a lesser extent, environmental responsibility is rewarding in more ways than one.” (Orlitzky et al. 2003) 11
- 12. A bit about ethics. Summary • There are many ethical perspectives. We all have our own view on the rightness/wrongness of different actions. • Ethical theory is all very well, but putting it into practice is difficult. The world is a messy mixed up place. • The one thing that can be said to apply across all ethical frameworks: – An ethical action is one which the perpetrator can defend in terms of more than self interest. (Finlay 2000). • Ethics pays. A well thought out, well implemented ethical corporate policy benefits both organizations and consumers/individuals in the long run. 12
- 13. 13 Agenda 1. Introduction 2. A bit about ethics 3. Ethics and personal data 4. Ethics for machine learning and automated decision making. A risk management framework
- 14. Ethics, data and Machine learning Whose data is it anyway? 14 Utilitarian orientated perspective Kantian/Rights based perspectiveMy data is a resource to be harvested and put to use. Constraints (laws) to prevent specific abuses and misuse of my data. My data is a part of who and what I am. It’s mine! My data should be treated with respect, just as I expect to be treated with respect. I will decide how data about me is used. You have no right to use my data without my permission. Better data & predictions = better outcomes. Everyone benefits.
- 15. Ethics, data and decision making. Whose data is it anyway? 15 Approach Pros Cons Utilitarian orientated perspective • More/better data means better decision making. • More get the very best deals (if they warrant it). • Social benefits. More data to support national / community initiatives (e.g. medical research and counterterrorism). • Best for the economy. • People less in control of their own destinies. • Better predictions does not always equate to increased in well-being. • The have-nots have even less. • Once the data is out there, it’s out there for good. Kantian/Rights based perspective • Each individual has control over their data and the uses to which it is put. • Less social exclusion. • Right to change/withdraw permission to use data, including “Right to be forgotten.” • Poorer decisions for individuals may result, if data is withheld or otherwise unavailable. • Lower economic benefits. • Society as a whole may suffer because large scale studies are data limited. (e.g. medical research and counter terrorism).
- 16. Ethics, data and decision making. Is more data and better prediction always better? 16 • More/better data leads to the promise of near perfect predictions in some areas. Is this a good thing? • Sometimes: – Identify terrorist subjects with high degree of certainty – Predict that a heart attack is very likely in the next 24 hours – Long term compatibility on a dating site – ….. • But not always – Near perfect insurance claim predictions are no benefit to anyone (except the insurer) – Do I want to know, years in advance, when I am likely to die? – …..
- 17. Ethics, data and decision making. Whose data is it anyway? 17 What’s the direction of Travel? USA, has to date, followed a more utility based model. Use data for whatever you want, but we will legislate where needed. EU has taken a rights based approach, and looks like it will continue to do so, with the General Data Protection Regulation (GDPR) which will come into force in 2018 in EU/UK.
- 18. 18 Agenda 1. Introduction 2. A bit about ethics 3. Ethics and personal data 4. Ethics and automated decision making. A risk management framework
- 19. Ethics, data and decision making. What data to use when? • Age • Alcohol consumption • Credit history • Criminal records • Dependents • DNA • Driving speed • Education • Gas consumption • Gender • Grocery purchases at supermarket • Income • Last book purchased • Live with smoker (Y/N) • Marital status • Medical history • Music currently listening too • Race • Religion • Sexual orientation • Smoker (Y/N) • Type of car you drive 19
- 20. Ethics, data and decision making. 1. Immutability of data? 20 Immutable (Individual can’t change at all) Mutable (Individual can change easily) Age Alcohol consumption IncomeCriminal record Gas consumption Education Gender Grocery purchases Last book purchased Live with smoker Marital status Medical history Dependents Race Religion Music currently Listening too Sexual orientation Smoker Type of car Driving speed DNA
- 21. Ethics, data and decision making. 2. Beneficiary? 21 Individual / society Decision maker Treatment for illness Selection for tax inspection Product marketing Benefit payment Foreclosure Match on dating site Credit granting Child protection Insurance pricing For whose benefit is a decisions made ? (This is not the same thing as if the individual benefits from the decision) Suspect selection in criminal cases Making job offers Redundancy selection Home improvement grants Parole Survey selection
- 22. Ethics, data and decision making: 3. Impact 22 What is the potential impact of decisions on an individual’s well being? 22 Low Impact High Impact Treatment for illness Selection for tax inspection Product marketing Benefit payment ForeclosureMatch on dating site Credit granting Child protection Insurance pricing Suspect selection in criminal cases Making job offers Redundancy selection Home improvement grants Parole Survey selection
- 23. Ethics, data and decision making. Risk in decision making 23 1. Immutability of data 3. Impact on individual 2. Beneficiary of decision Decision maker Individual Immutable Mutable Low High
- 24. You need to decide what’s most important within your ethical view (i.e. column order). Impact of decision on individual Beneficiary of the decision Immutability of data used Ethical challenge / risk High Decision maker High Greatest Least Low Individual High Low Low Decision maker High Low Individual High Low • More legislation • Audit & regulatory oversight • Public interest • Greater manual involvement • Simple and explicable models • Judgemental overriding • Expert “Buy-in” • Understand model weaknesses • Constant monitoring/feedback • Less legislation • Predictive ability trumps all else • Complex “black box” models • Automated model generation • Rapid redevelopment of models • Little oversight E.G, foreclosure, redundancy, parole E.G. Marketing applications, Music playlists
- 25. Ethics, data and decision making: Alternative perspective… • It’s nothing to do with the data or the decision maker… • It’s how you make the decision that’s important… – Impartial, data driven process = GOOD (Ethical) – Biased/judgemental decision = BAD (Unethical) 25 Example: If women more likely to do X or Y than men (or vice versa), then it’s fine for Gender to feature in a predictive model, if that’s what the data is telling us. However, this view is not popular, at least not in the UK or EU. As evidenced by (fairly) recent decisions on the use of Gender in insurance, despite gender being one of the most predictive data items for all sorts of insurance claim behaviour.
- 26. In Summary • Ethical data use and decision making brings its own rewards • An ethical strategy is about more than just following the law. – Ethical and legal is where you want to be… • Some things to consider when formulating an ethical data and decision making policy: – The immutability of the data that you use. – The impact that your decisions will have on individuals. – The beneficiaries of the decisions you make. 26
- 27. Bibliography and further reading • Boatright, J. (2014) Ethics in Finance (3rd Edition). Wiley • Finlay, P. (2000). An introduction to Business and Corporate Strategy. Pearson Education. • Finlay, S. (2014). Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and methods. Palgrave Macmillan. https://www.amazon.co.uk/Predictive-Analytics-Data-Mining- Misconceptions/dp/1137379278/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=14 92778632&sr=8-2 • O’Neil, C. (2016). Weapons of math Destruction. How Big Data Increases Inequality and Threatens Democracy. Allen Lane. • Orlitzky, M., Schmidt, F. L., Rynes, S. L. (2003). Corporate Social and Financial Performance: A Meta-analysis. Organization Studies, volume 24, number 3, pages 403-441. • Siegel, E. (2016). Predictive Analytics: the Power to Predict Who Will Click, Buy, Lie, or Die. (2nd Edition). Wiley. 27
Notes de l'éditeur
- Hi My name is Steven Finlay. I’m head of Analytics at a company called Computershare Loan Services (CLS). You have probably never heard of CLS – but it’s Europes largest mortgage outsourcing provider, based in Skipton, Yorkshire. CLS is part of the Computershare Group, which employs over 15,000 people at several sites around the world. We specialize in providing the infrastructure to support residential mortgage portfolios from origination through to debt recovery. We currently manage more than £70 billion assets for our customers. Prior to this role I worked for a number of organizations including UK Government, Jaywing, The Cooperative Bank and Experian. I’m also a visiting (unpaid!) research fellow at the Lancaster University Management School, and it is in that capacity that I am speaking to you today. By trade I’m a data scientist. For the last 20 years I’ve spent most of my time building or designing decision making systems based around predictive analytics, mostly but not exclusively in financial services. In the early 2000s I began to get interested in some of the ethical aspects what I was doing in relation to automated decision making relating to consumer credit (i.e. credit scoring), but this has expanded to cover a much broader set of applications of automated decision making and it’s impact on individuals. A lot of what you are going to hear today is aligned some of the material in my latest book: Predictive analytics, Data Mining and Big data. Myths, misconceptions and Methods, Which is published by Palgrave Macmillan.
- So although many of you are primarily concerned with “credit scoring” and other types of model for credit risk assessment, this is a much broader topic that I’m talking about today that applies to any type of decision making system used to assess individuals. Also not region specific.
- Following a brief introduction I’m going to start with a bit about ethical theory. Given we only have an hour or so, I’m not going to be going into much detail, but I think this is important because in my experience, ethical considerations are not always at the forefront of the minds of data scientists. I’m not saying that data scientists are not any more or less ethically minded than anyone else, but often they get caught up in the technical/business problems that they are trying to address – no different from many other disciplines. It’s not often, in my experience, that they (or their managers) really question the ethical nature of what they are doing because they are too focused on the immediate tasks at hand. The world could perhaps do with a few more Edward Snowdens! I’ll then go on to talk a bit about the application of ethics to personal data and automated decision making, driven by predictive analytics/machine learning.
- We have lots of existing legislation, such as equality laws, consumer credit act, Data Protection etc. Also because, it’s data driven and based on mathematical principles, credit scoring and other types of prediction models are unbiased? So no need to worry about them?
- This is an important area, given that we are now seeing a whole raft of “AI / Machine learning” applications being developed and deployed across society based on predictive models, which are being used to make automated decisions about people and how to treat them. So while the material I’m going to talk about today started out as a discussion around credit scoring, it’s much broader than that and is generally applicable to almost any area of society, where automated decision making tools are now being deployed. Who to hire or fire Which prisoners to grant parole to. Dating sites Identification of terrorist suspects. Disease identification We have lots of existing legislation, such as equality laws, consumer credit act, Data Protection etc. Who to hire or fire Which prisoners to grant parole to. Identification of terrorist suspects. Disease identification
- Ethics is not a precise topic. It’s not well defined and quantitative. For people used to thinking and working in a “Data Driven” analytical way; i.e. people who live and breath Data and Analytics, then Ethical theory is not something that fits well with their natural way of thinking. Ethical behaviour is often used as something of a catch all, but people often get confused as to its meaning. Ethics is not about feelings, nor is it something confined to (religious) belief systems or the rule of law. It’s something much broader that affects each one of us every day in our relationships with others and how we behave as a society. This is a definition of ethics I found on Wikipedia – and like other areas of philosophy it’s easy to get tied up in the language. However, put more simply ethics is about the appropriateness of how we behave and what we do. Its about right and wrong. If you do the right thing you are being ethical, if you do the wrong thing you are not behaving ethically. Ethics is also something that is subjective and personal. No two individuals will share exactly the same ethical perspective and can different culturally. So in the UK the idea of offering a payment of some sort to get a government department to do something would be seen as unethical by most people (and be illegal). Yet in other countries it’s the norm for what we would call a bribe to be offered, and in some situations things won’t get done unless a suitable payment is made. Therefore I’m not going to preach to you today about what is right (ethical) and what is not (unethical). What I am going to do is talk about some of the different ethical perspectives that can be adopted, and some of the arguments over data and how it is applied within the context of automated decisions making. I’ll then talk to you about my view of ethical data usage and decision making using that data. You may agree with my view, you may not. My objective is put forward a particular ethics, or for you to agree with my ethical perspective. However, if I have encouraged you to think about ethical issues around data and decision making, then I’ll be more than happy.
- As I say, no two people have exactly the same perspective as to what’s right and wrong, but there are a number of ethical frameworks into which people’s perspectives on ethics can be placed. This diagram summarizes some of the common ethical frameworks that people adopt. This is by no means comprehensive, but a very common differentiator between how people view the rightness or wrongness of actions is whether they approach a particular issue from a consequentialist or non-consequentialist perspective. A consequentialist approaches things in terms of outcomes. The means justifies the ends. There is no such thing as a wrong action, only a wrong outcome. A non-consequentialist is more about principles and the way that things are done. It’s more about the journey and how you get there. So a classical example of the difference between a consequentialist and non-consequentialist approach is the use of nuclear weapons at the end of the second world war. The consequentialist view is very clear. The use of nuclear weapons was a quick way to bring the war to a conclusion, saving many American and Japanese lives that would otherwise have been lost in a long and drawn out land invasion of Japan. The non-consequentialist perspective is that the inhumane nature of the weapons and their long term impact on civilians and the environment was unjustified, even if it meant more lives being lost in the short term. There are several types of consequentialist, but probably the most popular consequentialist ethical theory is Utilitarianism. An ethical action is a good one if the overall net benefit; i.e. wellbeing and happiness of a society is improved. Measuring happiness and “well being” is difficult and for a classical utilitarian, benefit is measured in terms of maximizing utility in terms of monetary outcomes and cost/benefit – it’s all very black and white. Another example is consequentialism is Hedonism – maximising pleasure. If we now turn to the right hand side of the diagram, then things are very different. One view of ethics comes from religious teaching. God is a better judge of right and wrong than people and the right way to do things is specified in scripture. E.g. the ten commandments “do not kill”, “do not steal” and so on. Immanuel Kant’s was a famous German philosopher who lived in the eighteen century, and formed an ethical theory based on the principle of duty and respect for others. Kant argued that an action is only ethical if it shows respect for others. In other words, one should act in good faith, out of a sense of duty as to what is right and proper, which Kant termed the categorical imperative. Ethical is something all rational people would agree with. So a great example is the ethical way to divide up a chocolate cake amongst a lot of people. The only rational thing that everyone would agree with is to split the cake equally. Kant's also expressed his view on ethics in terms of the Golden rule, which had previously been expressed by Confucius, the Chinese's philosopher many centuries before “Do unto others as you would have done unto you.” The golden rule is however, not very useful in practice, because it implies that you should do things like giving all your money to complete strangers, because that is what you would want them to do. Consequently, it’s the inverse of the golden rule that is often most insightful; i.e. “Do no evil” which for a long time was Google’s unofficial company motto (dropped in 2015). Aristotle was also interested in Ethics – writing a whole book on the subject more than 2,300 years ago. He believed that people should operate in certain ways such as being a good parent or employee. Over the next two millennia these ideas evolved into what we today would describe as peoples’ basic (human) rights – the right to life, liberty and property as captured in the French Declaration of the rights of man and the US constitution. Human rights and Kant’s ethical theory often go hand in hand. A worker has a right to decent wages, and an employer has a duty to pay decent wages. A worker
- Ethical theory is all very well, but all ethical frameworks have their weaknesses. Utilitarian arguments have been used to excuse some of the worst atrocities in history. Communism and Utilitarianism are not the same thing, but Stalin justified his slaughter of millions as a means to achieve a communist utopia in which everyone would live idea lives. Achieving the ends by any means necessary. One can argue a similar line for the Khmer Rouge in Cambodia I tried to find some pictures of a soviet utopia, but for some reason not many seem to exist…. So I did the next best thing and found a picture of the now grade II listed Preston Bus Station as a substitute for cold war soviet architecture. Example of French “Brutalist” (raw). Anyway, back to the subject at hand, likewise religion has been used as an excuse for many wars and acts of terrorism throughout history. To follow a Kantian/rights based philosophy can be problematic, leading to some preventable disaster because certain actions where not taken. This is the justification often used by governments for ignoring an individual’s human rights. For example, the indefinite holding of “suspected terrorists” at Guantanamo bay, and more recently the mass surveillance programs of the US government is justified on the basis of the “Greater good”.
- That’s all very well, but is all this ethical theory any use to me? If we lived in an ideal world where the law is the embodiment of ethical behaviour then a “just follow the law” attitude to ethics would be an easy case to argue. However what you find in practice is that laws while often conceived with ethical behaviour in mind, are often compromises or become dated. The result is that while there is often a degree of overlap between what’s legal and what’s ethical, something is not necessarily ethical just because it’s legal. Look it up in a theorsus if you want! So to give you some examples. In the UK it’s legal to give alcohol to a five year old. So if your child’s infant school decided to set up tasting sessions for five year olds, that would be within the law, but I don’t think that many parents would agree with it. Another case is Tax avoidance. So recently there was a lot of press over Starbucks (and many others) paying no UK tax by shuffling their profits overseas. Starbucks and others were not acting illegally, but much of the public feeling was that this was unethical. Yet for an accountant working within the firm, their duty to their employer was to legally minimize tax. If an accountant had said “You could have paid $100m less tax, but I thought that would have deprived the country of a couple of hospitals” then that’s not very ethical in that context, plus the accountant would have been fired.
- Even if it’s not a legal requirement, it pays to be ethically minded in business. The largest ever meta-study of the relationship between ethical performance (corporate social responsibility) and financial performance shows a clear and immediate link between ethical behaviours and financial outcomes. You may be following the law, but if people don’t like what you do, then you will suffer for it.
- In practice we all adopt different bits of these different ethical theories, and apply them in different ways to different problems and scenarios. For me I think that the utilitarian approach is probably a good one for deciding resource allocation for many infrastructure projects, but I feel that it’s a lot more about rights and personal dignity when talking about how best to allocate resources within the NHS. Regardless of your personal ethics, one thing that I think can be said to apply across all ethical frameworks, is that to act ethically, one must at the very least consider the impact of one’s actions on others. If you only justify what you do in terms of making a buck, or some other self-serving interest, then the morality of that act is questionable.
- What always comes up in debate about data, before anyone begins to think about how data is used, is the privacy and ownership of that data. The press is full of it. If you approach things from a utilitarian perspective, then you can view data as a resource that’s out there to be harvested and put to use. The default position is that data is put to good productive use. If however, there is a problem then we’ll legislate to control it. More data is good because it means better information, better predictions about behaviour and hence better decision making. I get a much better deal on my car insurance and mortgage interest rate because the information you have about me tells you that I’m low risk. If that information was not available I would pay more, and I’d be subsidising those who display high risk behaviours. With a rights based perspective, the outcomes are far less important. It’s my data and you should respect it. You have no right to it. If I withdraw my right for you to use it, then tough!
- So the “open data,” utilitarian view, gives one the optimum outcomes in terms of predictive accuracy and decision making and the economy. However, what you tend to see is a polarisation. Those with “Good” data get a great deal, which is probably most of us, but the rest get a very poor deal or no deal at all. And from a utilitarian perspective, that’s how it should be. The rights based view on the other hand, puts more control in the hands of individuals, and arguably leads to less social exclusion and the research evidence is that a more balanced society is a happier society. However certain things are not as optimal as they could be and there are economic impacts. For example there are no doubt many great benefits from wider access to medical records, but we’ve had the issue of NHS data being released for medical studies, but so poorly handled…. The “right to be forgotten” case in the EU, which is being incorporated into the General Data Protection Regulation (GDPR), supports a rights based view, but profit making organizations initially expressed their “amazement” and “fury” about that decision. In particularly US companies such as
- Is more data/ better decision making always good? So this somewhat counters the “gather and use it all” perspective http://deathclock.com/
- So what you tend to see in the US is that existing laws are brought to bear e.g. right to privacy under US constitution OR very specific laws are enacted. For example, Fair Credit Reporting Act (1970) and Health Insurance portability and Accountability act 1996. HIPAA (1996). The Driver's Privacy Protection Act of 1994 (also referred to as the "DPPA"), Title XXX of the Violent Crime Control and Law Enforcement Act, is a United States federal statute governing the privacy and disclosure of personal information gathered by state Departments of Motor Vehicles. This has and does cause problems when US based organizations have tried to apply American thinking in EU member states – Google, Amazon and Facebook have all suffered reputational damage through their arguably over-zealous drive to gather data about people. It also means that if a UK based company wants to compete in the US, then it needs to think if it needs to change its attitude to personal data within that market – or otherwise face being at a disadvantage. The new EU legislation means that Non-European companies will have to stick to European data protection law if they operate on the European market. New EU laws are also introducing concepts such as “The right to be forgotten.” which organizations will need to decide how they are going to implement.
- What I’m now going to do is talk about a personal perspective, that I try and adopt when I’m dealing with data and decision making using predictive analytics, based on that data. This is something that I have thought about a lot over the years when working on various projects for a variety of public and private sector organizations. After thinking about the ethical angle, I then blend this with relevant legislation to come up with what I believe is ethical and satisfies relevant laws and regulations. So this is very much about my model of data, decision making and what’s ethical. So as I said at the beginning, you may agree with me, you may not, but I’ll be happy as long as it gives you something to take away and think about. So if I had some data items such as this, what would I do? How do I classify the data – what sort of lens would I apply when considering it?
- One of the first things I think about is the immutability of the data at my disposal. How changeable is it? Is it something that the individual was born with and/or can’t change, or something that is very much down to life choices? Things like your DNA and ethnic origin are not things you can change at all. At the other end of the scale things like what books and CDs you buy are very much within your control. However its not all black and white. Immutability is more of a spectrum. There are lots of things that in theory an individual could change or do differently, but there are various huddles and difficulties to overcome. I can marry, divorce, marry again as often as I like, but there are social, financial and legal barriers that make it difficult to do this very often. Likewise I don’t have to live with a smoker, but it’s a lot of effort to change that.
- The second thing I think about is who is going to benefit from the decisions that are made, based upon the data I hold about them?. I look at this from the perspective of the decision maker (i.e. the user of the predictive model upon which the data is based) and the individual (about whom decisions are being made) This perspective varies greatly if you are working in public or private sector, and in what capacity decisions are being made.
- The third angle I consider is the impact of the decision, and the magnitude of that decision individuals and how that will affect their well-being
- If we these three things together, then we end up with a three dimensional view of decision making An this is what I use when thinking about the ethical dimensions to decision making and the risks associated with those decisions
- I’ve prioritised these three dimensions of data and decision making as shown here – you may have a different view The book within which this table appears actually puts the beneficiary first, but when I had a rethink when putting this presentation together. For example, if the impact of the decision is high and the decision is for someone other than the individual, and that decision is based on highly immutable data – then that is an ethically challenging decision to make. I’m not saying you can’t use highly immutable data in such circumstances, but you need to think very carefully about how defensible your position is. At the other end of the spectrum, if all I’m doing is target people who might be interested in using public facilities such as libraries and parks – then there is much less challenge to that sort of decision.
- Example, and you’ll find that sort of thing in some types of model. However, it’s not a popular view.
- This is of course, over and above any legal requirements.