用距离协方差衡量预测与敏感属性独立性，提升模型公平性
Fairness via Independence: A (Conditional) Distance Covariance Framework
- 基于距离协方差度量预测与敏感属性的独立性
- 在训练中加入距离协方差惩罚项，显著缩小公平性差距
- 支持并行计算，理论保证批量计算收敛性
我们从统计角度探索公平性，通过选择性使用条件距离协方差或距离协方差统计量来评估预测与敏感属性之间的独立性。通过在模型训练中引入基于距离协方差的惩罚项，提升公平性。此外，我们提出了经验（条件）距离协方差的矩阵形式，支持并行计算以提高效率。理论上，我们证明了经验与总体（条件）距离协方差之间的收敛性，为批处理计算提供了必要保障。在多个真实数据集上的实验表明，该方法能有效缩小机器学习中的公平性差距。代码已开源。
原文摘要 · Abstract (English)
We explore fairness from a statistical perspective by selectively utilizing either conditional distance covariance or distance covariance statistics as measures to assess the independence between predictions and sensitive attributes. We boost fairness with independence by adding a distance covariance-based penalty to the model's training. Additionally, we present the matrix form of empirical (conditional) distance covariance for parallel calculations to enhance computational efficiency. Theoretically, we provide a proof for the convergence between empirical and population (conditional) distance covariance, establishing necessary guarantees for batch computations. Through experiments conducted on a range of real-world datasets, we have demonstrated that our method effectively bridges the fairness gap in machine learning. Our code is available at \url{https://github.com/liuhaixias1/Fair_dc/}.
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