A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification

Bradley Rava, Wenguang Sun, Gareth M. James, Xin Tong

Journal of the American Statistical Association · Thu

DOI: 10.1080/01621459.2026.2634937

Abstract

We investigate the fairness issue in classification, where automated decisions are made for individuals from different protected groups. In high-consequence scenarios, decision errors can disproportionately affect certain protected groups, leading to unfair outcomes. To address this issue, we propose a fairness-adjusted selective inference (FASI) framework and develop data-driven algorithms that achieve statistical parity by controlling the false selection rate (FSR) among protected groups. Our FASI algorithm operates by converting the outputs of black-box classifiers into R-values, which are both intuitive and computationally efficient. These R-values serve as the basis for selection rules that are provably valid for FSR control in finite samples for protected groups, effectively mitigating the unfairness in group-wise error rates. We demonstrate the numerical performance of our approach using both simulated and real data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Keywords

Calibration by group, Fairness in machine learning, False selection rate, Selective Inference, Statistical parity

Enable JavaScript for citation tools and additional paper interactions.