提出可通用的公平回归最优性验证方法，支持事后优化。
Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
- 构建适用于多种场景的元定理，验证公平回归最优性
- 证明公平最优回归可通过事后处理实现
- 让传统回归改进可直接用于公平建模，降低开发门槛
我们研究了在人口均等性约束下的回归问题，这是常用的一种公平性定义。近期研究已发现符合该约束的公平极小极大最优回归算法，即最精确且满足公平性的算法。然而，这些分析紧密依赖于特定的数据生成模型。本文提出可应用于多种情境的元定理，用于验证相应回归算法的公平极小极大最优性。此外，我们证明公平极小极大最优回归可通过事后处理方法实现，使研究人员和实践者能够专注于改进常规回归技术，进而高效地将其适配为公平回归。
原文摘要 · Abstract (English)
We address the regression problem under the constraint of demographic parity, a commonly used fairness definition. Recent studies have revealed fair minimax optimal regression algorithms, the most accurate algorithms that adhere to the fairness constraint. However, these analyses are tightly coupled with specific data generation models. In this paper, we provide meta-theorems that can be applied to various situations to validate the fair minimax optimality of the corresponding regression algorithms. Furthermore, we demonstrate that fair minimax optimal regression can be achieved through post-processing methods, allowing researchers and practitioners to focus on improving conventional regression techniques, which can then be efficiently adapted for fair regression.
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