In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race. We call this class of problems \emph{fair regression}. We propose general schemes for fair regression under two notions of fairness: (1) statistical parity, which asks that the prediction be statistically independent of the protected attribute, and (2) bounded group loss, which asks that the prediction error restricted to any protected group remain below some pre-determined level. While we only study these two notions of fairness, our schemes are applicable to arbitrary Lipschitz-continuous losses, and so they encompass least-squares regression, logistic regression, quantile regression, and many other tasks. Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions. In addition to analyzing theoretical properties of our schemes, we empirically demonstrate their ability to uncover fairness--accuracy frontiers on several standard datasets.

本文研究了一类称为公平回归的问题，在保证对于某些受保护属性（如性别或种族）的公平性的情况下，预测一个实数目标（如风险评分或再犯率）。提出了通用的公平回归方案，包括统计平等和有界组损失两种公平性。与其他任务相比，该方案只需要使用标准的风险最小化算法（如标准分类或最小二乘回归），而提供得到的解的最优性和公平性的理论保证。除了分析方案的理论性质外，还在几个标准数据集上经验性地展示其揭示公正 - 准确性前沿的能力。

公平回归：量化定义和基于约简的算法