A Generic Framework for Conformal Fairness
This work addresses the unfair coverage problem of conformal prediction (CP) on data containing sensitive attributes. We formally define “conformal fairness” as a constraint on the disparity in marginal coverage across sensitive groups. We propose the first theoretically grounded conformal fairness framework, which relaxes the standard i.i.d. assumption to accommodate non-i.i.d. structured data—such as graphs. Our method integrates exchangeability assumptions, group-wise calibration, and adaptive confidence adjustment to jointly control both coverage validity and fairness. Experiments on graph and tabular datasets demonstrate that our approach strictly satisfies the theoretical coverage guarantee while reducing inter-group coverage disparity to within a user-specified threshold. It consistently outperforms existing baselines in both fairness and calibration fidelity.