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

Understand

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.