Empirical fair auditing of high-confidence failures under distribution shift

Fair auditing of high-confidence failures is crucial for validating the trustworthiness of machine-learning decision-support systems operating under distribution shift. While selective classification and out-of-distribution detection have attracted growing interest, three core challenges remain unresolved. Existing abstention methods suffer from the following limitations: they do not explicitly target high-confidence errors and therefore waste review budget on lower-confidence regions where such failures are unlikely; they are typically evaluated only at a single deployment snapshot, making them brittle under progressive distribution shift; and they ignore fairness, allowing subgroup disparities in high-confidence failures to remain hidden. To address these issues, we propose an empirical fair-auditing framework for budget-constrained review under distribution shift. In particular, τ-gating confines monitoring to the τ-target eligible high-confidence region, aligning alert selection with the audit objective and reducing budget waste, while fairness-aware alert allocation combines risk-based prioritization with parity and bounded group-allocation policies to reduce disparities across sensitive groups. The proposed approach is evaluated on ACSIncome, Diabetic Readmission, CivilComments, and a cross-domain text shift (CivilComments → Jigsaw) over 20 random seeds against standard baselines. The experimental results demonstrate an HC-AUC of 0.944 on Diabetic Readmission, Lift@5 of 9.518 on Diabetic Readmission and 4.561 on ACSIncome, and a reduction in alert-rate disparity of up to 99.8% under parity allocation at negligible utility cost, establishing a practical foundation for knowledge-driven, fairness-aware decision support in high-stakes AI systems.