论文标题
部分可观测时空混沌系统的无模型预测
Repairing Regressors for Fair Binary Classification at Any Decision Threshold
论文作者
论文摘要
我们研究了后期处理的问题，以在所有决策阈值下最大化公平的二进制分类。通过降低每个组的得分分布之间的统计距离，我们表明我们可以立即提高所有阈值的公平性能，并且我们可以这样做，而无需大幅度降低准确性。为此，我们引入了一种形式的分布平价衡量标准，该量度捕获了不同保护组的分类分布的相似程度。我们的主要结果是基于最佳运输提出了一种新颖的后处理算法，该算法可最大化分布均衡，从而在所有阈值下达到均等赔率或均等机会（如均衡的赔率或均等机会）的共同概念。我们在两个公平的基准上证明了我们的技术在经验上运作良好，同时也超越了相关工作中的类似技术。
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.
