Fair Regression: Quantitative Definitions and Reduction-based Algorithms
This addresses fairness in regression tasks for applications like risk scoring, though it is incremental as it adapts existing fairness notions to regression.
The paper tackles the problem of predicting real-valued targets while ensuring fairness with respect to protected attributes, proposing reduction-based algorithms for fair regression under statistical parity and bounded group loss, and empirically demonstrates their ability to achieve fairness-accuracy trade-offs on standard datasets.
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.