A Fairness-Aware and Bias-Resilient XAI Framework for Equitable Financial Decision-Making
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".