Rethinking Fair Anomaly Detection via Representation Learning

Fair anomaly detection (AD) methods typically trade detection accuracy for group fairness, especially under severe group imbalance. We argue that this trade-off is not inherent to fully unsupervised AD but stems from how fairness constraints distort learned representations. We identify two failure modes: vanishing protected-group influence under imbalance and dimensional collapse caused by overly restrictive fairness regularization. We show theoretically that many existing fairness regularizations dilute the protected group's gradients as imbalance grows and, even with the AD loss included, suppress all group-predictive features as the fairness coefficient increases. We propose FADIG, which combines an adaptively re-balanced autoencoder with a fairness-aware contrastive objective, and prove that it removes the dependence of group contributions on the imbalance ratio and makes collapsed representations suboptimal. On four image and tabular datasets, FADIG achieves higher accuracy with lower group disparity than existing methods.