Introduction

Ethics should make us joyful, not afraid. Ethics is not about what’s wrong, but what’s right. It speaks to us of the possibility of living our best life, of having aspirations that are noble and good, and gives us the means and tools to help realize that possibility. We spend so much more effort trying to prevent what’s bad and wrong when we should be trying to create something that is good and right.

Similarly, in learning analytics, the best outcome is achieved not by preventing harm, but rather by creating good. Technology can represent the best of us, embodying our hopes and dreams and aspirations. That is the reason for its existence. Yet, “classical philosophers of technology have painted an excessively gloomy picture of the role of technology in contemporary culture,” writes Verbeek (2005:4). What is it we put into technology and what do we expect when we use it? In analytics, we see this in sharp focus.

Ethics is based on perception, not principle. It springs from that warm and rewarding sensation that follows when we have done something good in the world. It reflects our feelings of compassion, of justice, of goodness. It is something that comes from inside, not something that results from a good argument or a stern talking-to. We spend so much effort drafting arguments and principles as though we could convince someone to be ethical, but the ethical person does not need them, and if a person is unethical, reason will not sway them.

We see the same effect in analytics. Today’s artificial intelligence engines are not based on cognitive rules or principles; they are trained using a mass of contextually relevant data. This makes them ethically agnostic; they defy simple statements of what they ought not do. And so the literature of ethics in analytics express the fears of alienation and subjugation common to traditional philosophy of technology. And we lose sight, not only of the good that analytics might produce, but also of the best means for preventing harm.

What, then, do we learn when we bring these considerations together? That is the topic of this essay. Analytics is a brand new field, coming into being only in the last few decades. Yet it wrestles with questions that have occupied philosophers for centuries. When we ask what is right and wrong, we ask also how we come to know what is right and wrong, how we come to learn the distinction, and to apply it in our daily lives. This is as true for the analytics engine as it is for the person using it.

And as we shall see, these are and continue to be open questions. It may seem that many writers approach the subject as though we have solved ethics. But we have not. There are multiple perspectives on ethics, and each issue that arises in learning analytics – and there are many – is subject to multiple points of view. We cannot simply say “solve this problem and we have solved the problem of ethics in learning analytics.”

Perhaps, it may be argued, we should focus specifically on outcomes. This is a common line of reasoning education circles, focusing for example on ‘what works’ (Serdyukov, 2017) and ‘effect sizes’ (Hattie, 2008). But as we shall see, it is no simple task to define successful outcomes, nor how to cause them. Will it work next time? What happens when we can’t predict what the secondary effects will be, and what happens when we can’t repair bad consequences after the fact?

Perhaps, it may be argued, we should focus specifically on rules or principles. This is a common line of reasoning in ethical circles, and especially professional ethics, where ethics in such fields are typically defined in terms of obligations and duties (Jamal & Bowie, 1995). We shall see, however, that universal principles do not take into account context and particular situations, they do not take into account larger interconnected environment in which learning analytics are used, and they do not take into account how analytics themselves work.

As we shall see, the key to understanding both ethics and analytics is to understand that they are not about something abstract and abstruse, but instead are about us – who we are, where we live, how we connect, what we believe, how we see the future. This is felt as a sensation or feeling of rightness and wrongness. In this context, what defines ‘ethical’ is a ‘duty of care’, the same sort of care that we have learned through the day-to-day experiences we have throughout our lives, through our interactions with others, through being connected, dependent and responsible for others.

And we shall see that the same sort of mechanism is at work in learning analytics, where it is neither possible nor desirable to over-rule the learning algorithm. We cannot, or at least should not, expect analytics to create a somehow corrected version of ourselves. If we want better learning analytics – whatever that means – then we have to become better people. Not ‘better’ in the sense that we conform rigorously to rules or principles, not ‘better’ in the sense that we always succeed, but ‘better’ in the sense that we care, where this means something like, being kind, being open, embracing diversity, and living in harmony.

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Ethics, Analytics and the Duty of Care by National Research Council Canada is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

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