ML Fairness Breaks Under Distribution Shift–Here's the Fix(arxiv.org)
arxiv.org
ML Fairness Breaks Under Distribution Shift–Here's the Fix
https://arxiv.org/abs/2509.25295
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C3F achieves group-conditional coverage parity under distribution shift without model retraining. This matters because every deployed ML system faces covariate shift, yet current fairness methods assume static distributions. The method provides finite-sample lower bounds on group-wise coverage with degradation proportional to chi-squared divergence between distributions. Empirical results show it outperforms existing fairness-aware conformal methods while remaining computationally efficient.