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arXiv · 2310.12785

A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier

Hua Tang·Lu Cheng·Ninghao Liu·Mengnan Du
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Abstract

While the accuracy-fairness trade-off has been frequently observed in the literature of fair machine learning, rigorous theoretical analyses have been scarce. To demystify this long-standing challenge, this work seeks to develop a theoretical framework by characterizing the shape of the accuracy-fairness trade-off Pareto frontier (FairFrontier), determined by a set of all optimal Pareto classifiers that no other classifiers can dominate. Specifically, we first demonstrate the existence of the trade-off in real-world scenarios and then propose four potential categories to characterize the important properties of the accuracy-fairness Pareto frontier. For each category, we identify the necessary conditions that lead to corresponding trade-offs. Experimental results on synthetic data suggest insightful findings of the proposed framework: (1) When sensitive attributes can be fully interpreted by non-sensitive attributes, FairFrontier is mostly continuous. (2) Accuracy can suffer a \textit{sharp} decline when over-pursuing fairness. (3) Eliminate the trade-off via a two-step streamlined approach. The proposed research enables an in-depth understanding of the accuracy-fairness trade-off, pushing current fair machine-learning research to a new frontier.

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Hua Tang, Lu Cheng, Ninghao Liu, Mengnan Du. 2023-10-19. A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier. https://arxiv.org/abs/2310.12785

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