Minimax Group Fairness: Algorithms and Experiments

Emily Diana · Wesley Gill · Michael J. Kearns · Krishnaram Kenthapadi · Aaron Roth

74 citations0/0 files storedProceedings of the AAAI/ACM Conference on AI Ethics and Society

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Ethics and Social Impacts of AI Privacy-Preserving Technologies in Data Machine Learning and Algorithms

Abstract

We consider a recently introduced framework in which fairness is measured by worst-case outcomes across groups, rather than by the more standard differences between group outcomes. In this framework we provide provably convergent oracle-efficient learning algorithms (or equivalently, reductions to non-fair learning) for minimax group fairness. Here the goal is that of minimizing the maximum loss across all groups, rather than equalizing group losses. Our algorithms apply to both regression and classification settings and support both overall error and false positive or false negative rates as the fairness measure of interest. They also support relaxations of the fairness constraints, thus permitting study of the tradeoff between overall accuracy and minimax fairness. We compare the experimental behavior and performance of our algorithms across a variety of fairness-sensitive data sets and show empirical cases in which minimax fairness is strictly and strongly preferable to equal outcome notions.

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