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Flexible Fairness Learning via Inverse Conditional Permutation
Yuheng Lai, Leying Guan
https://arxiv.org/abs/2404.05678 https://arxiv…

Flexible Fairness Learning via Inverse Conditional Permutation
Equalized odds, as a popular notion of algorithmic fairness, aims to ensure that sensitive variables, such as race and gender, do not unfairly influence the algorithm prediction when conditioning on the true outcome. Despite rapid advancements, most of the current research focuses on the violation of equalized odds caused by one sensitive attribute, leaving the challenge of simultaneously accounting for multiple attributes under-addressed. We address this gap by introducing a fairness learning …

This https://arxiv.org/abs/2310.05725 has been replaced.
link: https://scholar.google.com/scholar?q=a

Post-hoc Bias Scoring Is Optimal For Fair Classification
We consider a binary classification problem under group fairness constraints, which can be one of Demographic Parity (DP), Equalized Opportunity (EOp), or Equalized Odds (EO). We propose an explicit characterization of Bayes optimal classifier under the fairness constraints, which turns out to be a simple modification rule of the unconstrained classifier. Namely, we introduce a novel instance-level measure of bias, which we call bias score, and the modification rule is a simple linear rule on t…