修复回归器以在任意决策阈值下实现公平二分类
Repairing Regressors for Fair Binary Classification at Any Decision Threshold
AI总结：
本文提出基于最优传输的回归器后处理算法，通过缩小群体分数分布距离，在任意决策阈值下实现群体公平且保持准确率。
摘要：
我们研究了对监督式机器学习回归器进行后处理的问题，以在所有决策阈值上最大化公平二分类性能。通过减小各组分数分布之间的统计距离，我们表明可以同时提升所有阈值上的公平表现，而且不会造成准确率的大幅下降。为此，我们提出了一种形式化的分布公平性（Distributional Parity）度量，用于刻画不同受保护群体在分类结果分布上的相似程度。我们的主要成果是提出了一种基于最优传输的新型后处理算法，该算法可证明地最大化 Distributional Parity，从而在所有阈值上实现 Equalized Odds 或 Equal Opportunity 等常见的群体公平性概念。我们在两个公平性基准上证明，该技术在实证中表现良好，同时优于并推广了相关工作中的类似技术。
英文摘要：
We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds. By decreasing the statistical distance between each group's score distributions, we show that we can increase fair performance across all thresholds at once, and that we can do so without a large decrease in accuracy. To this end, we introduce a formal measure of Distributional Parity, which captures the degree of similarity in the distributions of classifications for different protected groups. Our main result is to put forward a novel post-processing algorithm based on optimal transport, which provably maximizes Distributional Parity, thereby attaining common notions of group fairness like Equalized Odds or Equal Opportunity at all thresholds. We demonstrate on two fairness benchmarks that our technique works well empirically, while also outperforming and generalizing similar techniques from related work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
