# Intersectional fairness

In intersectional fairness auditing, finer subgroup definitions leave fewer samples per subgroup, so an observed gap may be nothing but sampling noise. SAFER attaches the fairness penalty to a differentiable soft-count likelihood-ratio evidence statistic, applying gradient only to subgroup gaps beyond what noise can explain, and keeps a small fixed-weight ungated term to prevent optimization stalling. On tabular benchmarks including Adult, COMPAS and German Credit plus purpose-built stress variants, it gives a better fairness-utility frontier where certified disparities exist and preserves the utility of the unconstrained model where none do.

In intersectional fairness auditing, finer subgroup definitions leave fewer samples per subgroup, so an observed gap may be nothing but sampling noise. SAFER attaches the fairness penalty to a differentiable soft-count likelihood-ratio evidence statistic, applying gradient only to subgroup gaps beyond what noise can explain, and keeps a small fixed-weight ungated term to prevent optimization stalling. On tabular benchmarks including Adult, COMPAS and German Credit plus purpose-built stress variants, it gives a better fairness-utility frontier where certified disparities exist and preserves the utility of the unconstrained model where none do.