Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\texorpdfstring{$\delta$}{delta} Alignment

The paper introduces ReLiF, a framework designed to enhance Lipschitz fairness evaluation in multi-task learning (MTL) by employing fixed-$\delta$ auditing to mitigate threshold confounding. It utilizes a shared reference tolerance for auditing and a violation-rate feedback controller, facilitating a balance between fairness and utility during training. Experiments demonstrate that ReLiF, when applied to clinical time-series data and NYUv2 using a ResNet50 backbone, achieves competitive utility while significantly reducing bias, thereby providing a more reliable method for assessing fairness in MTL contexts.