FRAPP\'E: A Group Fairness Framework for Post-Processing Everything

arXiv:2312.02592v2 Announce Type: replace
Abstract: Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model. In these situations, post-processing is a viable alternative. However, current methods are tailored to specific problem settings and fairness definitions and hence, are not as broadly applicable as in-processing. In this work, we propose a framework that turns any regularized in-processing method …

abstract applications arxiv computation cs.cy cs.lg current error everything fairness framework pipeline post-processing practical prediction processing resources trade training training pipeline type