聚焦分布尾部实现公平，提升回归模型的公平性与准确性。
Demographic Parity Tails for Regression
- 仅对目标分布尾部施加公平约束，避免整体性能下降。
- 基于最优传输理论，实现可解释且灵活的公平干预。
- 适用于关注特定群体尾部风险的应用场景，如高危预测。
在回归任务中，人口统计均等性（Demographic Parity, DP）是一种广泛研究的公平性标准，要求预测结果与敏感属性独立。然而，对整个分布施加约束会降低预测准确性，且对许多应用而言并不必要，因为公平性关切通常局限于分布的特定区域。为此，我们提出一种新的回归公平框架，专注于敏感群体在目标分布尾部的公平性。该方法基于最优传输理论，仅在指定分布区域施加公平约束，从而实现更细致、上下文敏感的干预。结合最新进展，我们开发了一种可解释且灵活的算法，利用最优传输的几何结构。我们提供了理论保证，包括风险界和公平性质，并通过回归设置中的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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