Deep Fair Learning: Task-Aware Fair Representations via Joint Distance-Covariance Regularization
Deep Fair Learning (DFL) creates fair and informative data representations by combining nonlinear sufficient dimension reduction with deep learning. It enforces conditional independence between sensitive attributes and these representations using a fairness-promoting penalty during fine-tuning. This method achieves better fairness-accuracy trade-offs than existing techniques.