The Reach of Fairness

In conversation with Barocas, Hardt, and Narayanan’s Fairness and Machine Learning (FaML), we seek to broaden the scope of normative argument about machine learning and algorithmic decision making. Beginning from an understanding of fair cooperation among free and equal persons as a fundamental political value, we argue that concerns about fairness and machine learning need to be expanded in three ways. First, unfairness and discrimination are not only a matter of systematic group subordination. We consider other forms of unfairness that are not about disadvantaged groups but about removing barriers to opportunity, and suggest practical implications for algorithmic decisions. Second, while we broadly agree with FaML’s approach to fair organizational decisions, we underscore the limits of a focus on fair organizational decisions in advancing equality of opportunity in society. Finally, drawing on Rawls, we present aspects of a fair society that are not simply matters of equal opportunity, and consider some broader, under-explored ramifications of algorithms and AI on societal fairness. Specifically, we suggest the implications that AI deployment at scale has for fair distribution of income and wealth, political liberties, and public deliberation.