End-to-End Fairness Optimization with Fair Decision-Focused Learning
End-to-end fairness optimization (E2EFO) is introduced as a unifying framework that integrates fairness across the prediction-to-decision pipeline and fair decision-focused learning (FDFL), a training paradigm that jointly accounts for prediction accuracy, prediction fairness, and decision regret.