Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning

Yijin Ni, Xiaoming Huo

This paper introduces a novel kernel-based formulation of the Equalized Odds (EO) criterion, denoted as $E{O}_k$, for fair representation learning (FRL) in supervised settings. The c…