A Fairness-Aware and Bias-Resilient XAI Framework for Equitable Financial Decision-Making

Loan-approval prediction is typically considered while auditing a single protected attribute at a time (race, ethnicity, sex, or age).Fairness-Aware, Interpretable, Resilient, and Equitable (FAIRE) is a multi-stage pipeline that combines data-level balancing, in-training debiasing, and post-processing thresholding, which is complemented by global and local explainability and continuous fairness monitoring with a drift trigger.The evaluation spans centralized and federated training with privacy-preserving aggregation using Home Mortgage Disclosure Act (HMDA) loan-level data.At the selected operating point, fairness improves substantially: Demographic Parity (DP) rises from 0.74 [0.72, 0.76] to 0.92 [0.90, 0.94]; the Equal Opportunity (EO) gap declines to 0.05 [0.04, 0.06]; and Equalized Odds (EOdds) decreases to 0.07 [0.06, 0.09].The change in Area under the curve-Receiver-operating characteristic curve (AUC-ROC) changes by ≤ 0.5 percentage points relative to the best utility setting.In the federated regime (50 clients, Non-Independent and Identically Distributed (non-IID) partitions), AUC-ROC remains within 1 percentage point of centralized utility, while fairness remains close to centralized post-mitigation levels (e.g., DP ≈ 0.90 [0.88, 0.92], EO ≈ 0.06 [0.05, 0.07], EOdds ≈ 0.11 [0.10, 0.12]), with wider intervals for clients with small protected-group support sample sizes.A composite Interpretability Score increases through higher surrogate fidelity, sparser reason sets, and more stable attributions; SHapley Additive exPlanations (SHAP), Local Interpretable Modelagnostic Explanations (LIME), and Integrated Gradients produce adverse-action-ready reason codes consistent with threshold-style explanations.The resulting pipeline delivers measurable fairness gains with minimal utility cost across centralized and federated settings while maintaining transparent, monitorable credit decisions.