Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings
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 demographic parity penalty as an optimal transport problem. Our framework unifies both the \emph{aware} and \emph{unaware} settings and characterizes optimal prediction functions via optimal transport maps, under both squared Wasserstein-2 and Total Variation penalties. These results reveal that the choice of ...
| Authors | M. Generali Lince, V. Divol, R. Flamary et al. |
|---|---|
| Primary category | stat.ML |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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