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[2508.17622] The Statistical Fairness-Accuracy Frontier
Summary
This article explores the trade-offs between fairness and accuracy in predictive modeling, introducing the fairness-accuracy (FA) Pareto frontier and optimal estimators for finite samples.
Why It Matters
Understanding the fairness-accuracy trade-off is crucial for developing equitable machine learning models. This research provides insights into how to balance these competing objectives, which is increasingly important in diverse applications of AI, ensuring that models serve all demographic groups fairly.
Key Takeaways
- The fairness-accuracy (FA) Pareto frontier helps visualize trade-offs in predictive modeling.
- Optimal estimators can be derived even with limited data, improving model fairness and accuracy.
- Sample allocation strategies can significantly impact the welfare of different demographic groups.
Statistics > Machine Learning arXiv:2508.17622 (stat) [Submitted on 25 Aug 2025 (v1), last revised 16 Feb 2026 (this version, v3)] Title:The Statistical Fairness-Accuracy Frontier Authors:Alireza Fallah, Michael I. Jordan, Annie Ulichney View a PDF of the paper titled The Statistical Fairness-Accuracy Frontier, by Alireza Fallah and 2 other authors View PDF 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 for the entire FA frontier, yielding confidence bands that quantify the reliability of welfare comparisons across alternative fairness-accuracy tradeoffs. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Theor...