FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport

TL;DR

FairPOT introduces a flexible, model-agnostic post-processing method that balances fairness and AUC performance by selectively aligning risk score distributions across groups using optimal transport, with extensions for partial AUC scenarios.

Contribution

It proposes a novel, tunable post-processing framework leveraging optimal transport to improve fairness in risk scores while preserving AUC, adaptable to partial AUC settings.

Findings

Outperforms existing methods in fairness and AUC trade-offs.

Achieves fairness improvements with minimal AUC degradation.

Effective on synthetic, public, and clinical datasets.

Abstract

Fairness metrics utilizing the area under the receiver operator characteristic curve (AUC) have gained increasing attention in high-stakes domains such as healthcare, finance, and criminal justice. In these domains, fairness is often evaluated over risk scores rather than binary outcomes, and a common challenge is that enforcing strict fairness can significantly degrade AUC performance. To address this challenge, we propose Fair Proportional Optimal Transport (FairPOT), a novel, model-agnostic post-processing framework that strategically aligns risk score distributions across different groups using optimal transport, but does so selectively by transforming a controllable proportion, i.e., the top-lambda quantile, of scores within the disadvantaged group. By varying lambda, our method allows for a tunable trade-off between reducing AUC disparities and maintaining overall AUC performance. Furthermore, we extend FairPOT to the partial AUC setting, enabling fairness interventions to concentrate on the highest-risk regions. Extensive experiments on synthetic, public, and clinical datasets show that FairPOT consistently outperforms existing post-processing techniques in both global and partial AUC scenarios, often achieving improved fairness with slight AUC degradation or even positive gains in utility. The computational efficiency and practical adaptability of FairPOT make it a promising solution for real-world deployment.