Abstract: We investigate fairness 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 and equalizing 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. The selection rules based on R-values, which effectively mitigate disparate impacts on protected groups, are provably valid for FSR control in finite samples. We demonstrate the numerical performance of our approach through both simulated and real data.

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

Bio：Brad Rava is a Lecturer in the discipline of Business Analytics at the University of Sydney's Business School. His research focuses on Empirical Bayes techniques, Fairness in Machine Learning, Statistical Machine Learning, and High Dimensional Statistics.

Brad Rava’s research interests focus modern statistical methods for addressing pressing societal problems that arise from combining automated decision making with high-risk scenarios. To properly communicate uncertainty in these high-risk scenarios, Brad’s research has drawn upon Empirical Bayes techniques, Fairness in Machine Learning, Statistical Machine Learning, and High Dimensional Statistics.