This is the second in my series on common misunderstandings about predictive analytics that hinder their adoption in higher education. Last week I talked about the language of predictive analytics. This week, I want to comment on another common misconception: that predictive analytics (and educational data mining more generally) is a social science. Read more
In higher education, and in general, an increasing amount of attention is being paid to questions about the ethical use of data. People are working to produce principles, guidelines and ethical frameworks. This is a good thing.
Despite being well-intentioned, however, most of these projects are doomed to failure. The reason is that, amidst talk about arriving at an ethics, or developing an ethical framework, the terms ‘ethics’ and ‘framework’ are rarely well-defined from the outset. If you don’t have a clear understanding of your goal, you can’t define a strategy to achieve it, and you won’t know if you have reached it if you ever do.
As a foundation to future blog posts that I will write on the matter of ethics in AI, what I’d like to do is propose a couple of key definitions, and invite comment where my assumptions might not make sense.
What do we mean by ‘ethics’?
Ethics is hard to do. It is one of those five inter-related sub-disciplines of philosophy defined by Aristotle that also includes metaphysics, epistemology, aesthetics, and logic. To do ethics involves establishing a set of first principles, and developing a system for determining right action as a consequence of those principles. For example, if we presume the existence of a creator god that has given us some kind of access to true knowledge, then we can apply that knowledge to our day-to-day life as a guide to evaluating right or wrong courses of action. Or, instead of appealing to the transcendent, we might begin with certain assumptions about human nature and develop ethical guidelines meant to cultivate those essential and unique attributes. Or, if we decide that the limits of our knowledge preclude us from knowing anything about the divine, or even ourselves, except for the limits of our knowledge, there are ethical consequences of that as well. There are many approaches and variations here, but the key thing to understand is that ethics is hard. It requires us to be thoughtful about arriving at a set of first principles, being transparent, and systematically deriving ethical judgements as consequences of our metaphysical, epistemological, and logical commitments.
What ethics is NOT, is a set of unsystematicly articulated opinions about situations that make us feel uneasy. Unfortunately, when we read about ethics in data science, in education, and in general, this is typically what we end up with. Indeed, the field of education is particularly bad about talking about ethics (and of philosophy in general) in this way.
What do we mean by a ‘framework’?
The interesting thing about the language of frameworks is that it has the potential to liberate us from much of the heavy burden placed on us by ethical thinking. The reason for this is that the way this language is used in relation to ethics — as in an ‘ethical framework’ — already presupposes a specific philosophical perspective: Pragmatism.
What is Pragmatism? I’m going to do it a major disservice here, but it is a perspective that rejects our ability to know ‘truth’ in any transcendent or universal way, and so affirms that the truth in any given situation is a belief that ‘works.’ In other words, the right course of action is the one with the best practical set of consequences. (There’s a strong and compelling similarity here between Pragmatism and Pyrrhonian Skepticism, but won’t go into that here…except to note that, in philosophy, everything new is actually really old).
The reason that ethical frameworks are pragmatic is that they do not seek to define sets of universal first principles, but instead set out to establish methods or approaches for arriving at the best possible result at a given time, and in a given place.
The idea of an ethical framework is really powerful when discussing the human consequences of technological innovation. Laws and culture are constantly changing, and they differ radically around the globe. Were we to set out to define an ethics of educational data use, it could be a wonderful and fruitful academic exercise. A strong undergraduate thesis, or perhaps even a doctoral dissertation. But it would never be globally adopted, if for no other reason than because it would rest on first principles, the very definition of which is that they cannot themselves be justified. There will always be differences in opinion.
But an ethical framework CAN claim universality in a way that an ethics cannot, because it defines an approach to weighing a variety of factors that may be different from place to place, and that may change over time, but in a way that nevertheless allows people to make ethical judgments that work here and now. Where differences of opinion create issues for ethics, they are a valuable source of information for frameworks, which aim to balance and negotiate differences in order to arrive at the best possible outcome.
Laying my cards in the table (as if they weren’t on the table already), I am incredibly fond of the framework approach. Ethical frameworks are good things, and we should definitely strive to create an ethical frameworks for AI in education. We have already seen several attempts, and these have played an important role in getting the conversation started, but I see the language of ‘ethical framework’ being used with a lack of precision. The result has been some helpful, but rather ungrounded and unsystematic sets of claims pertaining to how data should be used in certain situations. These are not frameworks. Nor are they ethics. They are merely opinions. These efforts have been great for promoting public dialogue, but we need something more if we are going to make a difference.
Only by being absolutely clear from the outset about what an ethical framework is, and what it is meant to do, can we begin to make a significant and coordinated impact on law, public policy, data standards, and industry practices.