Overcoming the Resolution Limit: Significance-Aware Regularization for Intersectional Fairness

Intersectional fairness requires evaluating models across fine-grained subgroups. However, as group definitions become more granular, the sample size per subgroup shrinks, causing fairness estimates to become increasingly unstable. Existing in-processing methods are certainty-blind: they penalize observed disparities without distinguishing statistically certified unfairness from sampling noise. This leads to fairness overfitting, where models sacrifice utility to "fix" spurious disparities in sparse regimes. To address this, we introduce the significant-excess metric family, a continuous, uncertainty-aware fairness score that measures only the portion of a subgroup’s disparity exceeding what sampling variation can explain at a specific confidence level. Leveraging this, we propose SAFER (Significance-Aware Fairness Regularizer), a differentiable in-processing method that gates fairness penalties using a soft-count likelihood-ratio significance test. SAFER satisfies a provable safety property: when no subgroup disparity is statistically certifiable, its fairness gradient is exponentially small and training reduces to unconstrained optimization. Mechanism ablations across three benchmark datasets and purpose-built stress variants confirm that each component of SAFER – threshold smoothing, the significance gate, and a dense-tail term – is empirically necessary in its theoretically predicted operating regime. SAFER consistently achieves a superior fairness-utility Pareto frontier, compared to baselines methods in sparse intersectional settings while preserving utility when certified unfairness is absent.