Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings

ArXi:2605.28233v1 Announce Type: cross Fairness-accuracy trade-offs are a central concern in the deployment of fairness-aware machine learning methods. When sensitive attributes are unavailable at inference time-the so called unawareness setting, principled methods for obtaining accurate predictions under relaxed fairness constraints are largely missing. In this work, we address this gap by formulating regression under a graphic parity penalty as an optimal transport problem.