PPE Speaker Series: “Algorithmic Fairness” with Brian Hedden (ANU)

Abstract: Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of false positives be equal across the relevant groups. We might seek to ensure that algorithms satisfy all of these purported fairness criteria. But a series of impossibility results shows that this is impossible, unless base rates are equal across the relevant groups. What are we to make of these pessimistic results? I argue that none of the purported criteria, except for a calibration criterion, are necessary conditions for fairness, on the grounds that they can all be simultaneously violated by a manifestly fair and uniquely optimal predictive algorithm, even when base rates are equal. I conclude with some general reflections on algorithmic fairness.

Speaker Bio:

Brian Hedden is Professor of Philosophy at ANU. Before joining ANU, he was a PhD student at MIT, a postdoc at Oxford, and a faculty member at the University of Sydney. He works in epistemology and decision theory, as well as related areas of ethics and political philosophy. He is the author of Reasons without Persons (OUP 2015) and articles in Mind, Journal of Philosophy, Nous, Philosophy and Phenomenological Research, Ethics, and Philosophy and Public Affairs, among others. Most recently, he has published on legal standards of proof, cognitive biases, collective action problems, algorithmic fairness, and multidimensionality.