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The use of machine learning models in consequential decision making oftenexacerbates societal inequity, in particular yielding disparate impact onmembers of marginalized groups defined by race and gender. The area under theROC curve (AUC) is widely used to evaluate the performance of a scoringfunction in machine learning, but is studied in algorithmic fairness less thanother performance metrics. Due to the pairwise nature of the AUC, defining anAUC-based group fairness metric is pairwise-dependent and may involve both\emph{intra-group} and \emph{inter-group} AUCs. Importantly, considering onlyone category of AUCs is not sufficient to mitigate unfairness in AUCoptimization. In this paper, we propose a minimax learning and bias mitigationframework that incorporates both intra-group and inter-group AUCs whilemaintaining utility. Based on this Rawlsian framework, we design an efficientstochastic optimization algorithm and prove its convergence to the minimumgroup-level AUC. We conduct numerical experiments on both synthetic andreal-world datasets to validate the effectiveness of the minimax framework andthe proposed optimization algorithm.

Minimax AUC Fairness: Efficient Algorithm with Provable Convergence