Demographic parity (DP) is a widely studied fairness criterion in regression, enforcing independence between the predictions and sensitive attributes. However, constraining the entire distribution can degrade predictive accuracy and may be unnecessary for many applications, where fairness concerns are localized to specific regions of the distribution. To overcome this issue, we propose a new framework for regression under DP that focuses on the tails of target distribution across sensitive groups. Our methodology builds on optimal transport theory. By enforcing fairness constraints only over targeted regions of the distribution, our approach enables more nuanced and context-sensitive interventions. Leveraging recent advances, we develop an interpretable and flexible algorithm that leverages the geometric structure of optimal transport. We provide theoretical guarantees, including risk bounds and fairness properties, and validate the method through experiments in regression settings.

翻译：统计均等（DP）是回归任务中广泛研究的公平性准则，要求预测结果与敏感属性之间相互独立。然而，约束整个预测分布可能降低模型精度，且对许多仅需关注特定分布区域的公平性场景而言并非必要。为解决该问题，我们提出一种聚焦于不同敏感组目标分布尾部的回归DP框架。该方法基于最优输运理论构建，通过仅对分布的目标区域施加公平性约束，实现更具细粒度与场景适应性的干预。结合最新研究进展，我们开发了一种可解释且灵活的算法，利用最优输运的几何结构。本研究提供了包括风险界与公平性属性在内的理论保证，并通过回归实验验证了方法有效性。