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A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification by Rava, Bradley; Sun, Wenguang; James, Gareth M.; Tong, Xin is a scholarly article available to read on EtoBox.

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

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.

Author

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

Published

2021

Language

EN