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arXiv Machine Learning By Alireza Fallah, Michael I. Jordan, Annie Ulichney

The Statistical Fairness-Accuracy Frontier

Read the original on arXiv Machine Learning →

The paper investigates the tradeoff between fairness and accuracy when a single predictive model serves multiple demographic groups. It introduces the fairness-accuracy (FA) Pareto frontier, which identifies models that cannot be improved in either dimension without compromising the other. Focusing on finite-sample settings, the authors derive worst‑case‑optimal estimators, analyze how limited data asymmetrically affect each group’s welfare, and provide uniform bounds along the entire FA frontier.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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