Economic theories of distributive justice for fair machine learning

Abstract

Machine Learning is increasingly employed to make consequential decisions for humans. In response to the ethical issues that may ensue, an active area of research in ML has been dedicated to the study of algorithmic unfairness. This tutorial introduces fair-ML to the web conference community and offers a new perspective on it through the lens of the long-established economic theories of distributive justice. Based on our past and ongoing research, we argue that economic theories of equality of opportunity, inequality measurement, and social choice have a lot to offer-in terms of tools and insights-to data scientists and practitioners interested in understanding the ethical implications of their work. We overview these theories and discuss their connections to fair-ML.