Minimax Group Fairness: Algorithms and Experiments

TL;DR

This paper introduces algorithms for minimax group fairness that focus on minimizing the worst-case group loss, providing theoretical guarantees and empirical evidence of their effectiveness over traditional fairness measures.

Contribution

The paper develops provably convergent, oracle-efficient algorithms for minimax group fairness applicable to regression and classification, supporting various fairness measures and tradeoffs.

Findings

Algorithms effectively minimize maximum group loss.

Empirical results show minimax fairness outperforms equal outcome fairness.

Tradeoffs between accuracy and fairness are demonstrated.

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.