Minimax Group Fairness: Algorithms and Experiments
This work addresses fairness in AI for sensitive applications, offering a novel algorithmic framework with practical trade-offs, though it is incremental in building on existing fairness concepts.
The paper tackles the problem of group fairness in machine learning by proposing algorithms that minimize the maximum loss across groups, rather than equalizing group outcomes, and demonstrates through experiments that this minimax approach can be strictly preferable to traditional fairness notions in various datasets.
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.