The Statistical Fairness-Accuracy Frontier

Abstract:We study fairness-accuracy tradeoffs when a single predictive model must serve multiple demographic groups. A useful tool for understanding this tradeoff is the fairness-accuracy (FA) Pareto frontier, which characterizes the set of models that cannot be improved in either fairness or accuracy without worsening the other. While characterizing the FA frontier requires full knowledge of the data distribution, we focus on the finite-sample regime, quantifying how well a designer can approximate any point on the frontier from limited data and bounding the worst-case gap. In particular, we derive worst-case-optimal estimators that depend on the designer's knowledge of the covariate distribution. For each estimator, we characterize how finite-sample effects asymmetrically impact each group's welfare and identify optimal sample allocation strategies. Finally, we provide uniform finite-sample bounds along the entire FA frontier, quantifying finite-sample deviations across alternative fairness-accuracy tradeoffs.

Machine Learning (stat.ML); Machine Learning (cs.LG); Theoretical Economics (econ.TH); Optimization and Control (math.OC)

arXiv:2508.17622 [stat.ML]

(or arXiv:2508.17622v4 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2508.17622 Focus to learn more arXiv-issued DOI via DataCite

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