E-FairDrift: Anytime-Valid Tests for Intersectional Fairness Drift

A deployed model can look healthy on average while silently becoming unsafe for a small intersectional subgroup. We propose E-FairDrift, a sequential test for this failure mode. The method first discovers candidate subgroups from black-box signals - representations, confidence, residuals, or explanation embeddings - and then monitors each subgroup with an e-process, a non-negative supermartingale that remains valid under optional stopping induced by repeated interim analyses. At any common stopping or reporting time, e-BH converts the subgroup e-values into false-discovery-rate-controlled alarms under arbitrary dependence. For sparse labels, predictable audit sampling gives inverse-audited e-processes. In 30 no-shift seeds, no method raises an alarm; this is a sanity check rather than a standalone validation of 5\% FDR. At medium and high shift severity, E-FairDrift detects harmful latent subgroup drift in 53\% and 77\% of streams, respectively, substantially outperforming global monitoring while approaching an oracle monitor given the hidden sensitive groups.