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.

本文介绍了一个最小化最大损失的策略，用于处理团体公平性，并提供了能够支持回归、分类设置以及整体错误和误报率的相关算法。该算法还支持公平性约束的松弛，进一步研究了整体准确性与最小化最大公平性之间的权衡，并对多种数据集进行了实验分析，证明了最小化最大公平性严格和强烈优于平等结果概念。