论文标题
统计学习中公平性的投影
Projection to Fairness in Statistical Learning
论文作者
论文摘要
在回归的背景下，我们考虑了使估算值公平的基本问题，同时尽可能地保留其预测准确性。为此，我们将其对公平性的投影定义为其最接近的公平估计器，从而反映了预测准确性。我们的方法学利用了从最佳运输到有效构建的工具，可以作为一个简单的后处理步骤来构建任何给定估计器的公平性。此外，我们的方法精确地量化了公平成本，以预测准确性来衡量。
In the context of regression, we consider the fundamental question of making an estimator fair while preserving its prediction accuracy as much as possible. To that end, we define its projection to fairness as its closest fair estimator in a sense that reflects prediction accuracy. Our methodology leverages tools from optimal transport to construct efficiently the projection to fairness of any given estimator as a simple post-processing step. Moreover, our approach precisely quantifies the cost of fairness, measured in terms of prediction accuracy.
