用最优传输方法评估并实现条件公平性，突破多水平条件下的公平性难题。
Auditing and Enforcing Conditional Fairness via Optimal Transport
- 基于最优传输设计条件公平性度量，捕捉分布差异
- 在多水平条件和连续输出场景下实现完全条件分布对齐
- 适用于医疗、金融等需高精度公平性的实际场景
条件人口均等性（CDP）衡量的是在给定附加特征或特征集条件下，预测模型或决策过程的人口均等性。尽管已有多种算法公平性技术可实现人口均等性，但实现CDP仍极具挑战，尤其是在条件变量具有多个取值水平或模型输出为连续值时。现有文献对CDP的审计与实现研究不足。为此，我们提出新的条件人口偏差（CDD）度量，借鉴最优传输领域的统计距离。进一步设计并评估基于正则化的实现方法，包括\fairbit{}和\fairlp{}。这些方法可在条件变量取值较多时仍有效实现CDP。当模型输出为连续值时，我们的方法实现条件分布的完全相等，而非仅关注一阶矩或代理指标。我们在真实数据集上验证了方法的有效性。
原文摘要 · Abstract (English)
Conditional demographic parity (CDP) is a measure of the demographic parity of a predictive model or decision process when conditioning on an additional feature or set of features. Many algorithmic fairness techniques exist to target demographic parity, but CDP is much harder to achieve, particularly when the conditioning variable has many levels and/or when the model outputs are continuous. The problem of auditing and enforcing CDP is understudied in the literature. In light of this, we propose novel measures of {conditional demographic disparity (CDD)} which rely on statistical distances borrowed from the optimal transport literature. We further design and evaluate regularization-based approaches based on these CDD measures. Our methods, \fairbit{} and \fairlp{}, allow us to target CDP even when the conditioning variable has many levels. When model outputs are continuous, our methods target full equality of the conditional distributions, unlike other methods that only consider first moments or related proxy quantities. We validate the efficacy of our approaches on real-world datasets.
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