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

• 成果类型: Article; Early Access
• 署名作者: Rava, Bradley; Sun, Wenguang; James, Gareth M.; Tong, Xin
• 署名单位: University of Sydney; Zhejiang University; Zhejiang University; Emory University; University of Hong Kong
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2634937
• 发表日期: 2026-05-26
• 关键词: Calibration by group; Fairness in machine learning; False selection rate; Selective Inference; Statistical parity; False Discovery Rate; prediction; BIAS

摘要

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.