概念漂移下公平且可解释的信用评分：面向演化群体的自适应解释框架
Fair and Explainable Credit-Scoring under Concept Drift: Adaptive Explanation Frameworks for Evolving Populations
AI总结：
针对信用评分中概念漂移导致传统SHAP解释不稳定和不公平的问题，本文提出三种自适应SHAP变体，在保持预测性能的同时显著提升时间稳定性并减少群体间差异性影响。
摘要：
不断演化的借款人行为、变化的经济状况以及持续调整的监管环境，持续重塑现代信用评分系统背后的数据分布。传统的可解释性技术（如SHAP）假设数据是静态的且背景分布固定，这使得在概念漂移发生时，其解释变得不稳定并可能产生不公平。本研究通过开发自适应解释框架来应对这一挑战，该框架在动态演化的信用模型中重新校准可解释性与公平性。利用一个跨多年的信用数据集，我们将基于XGBoost的预测建模与三种自适应SHAP变体相结合：（A）按切片解释重加权，用于调整特征分布偏移；（B）漂移感知的SHAP重定基线，使用滑动窗口背景样本；（C）使用增量Ridge回归的在线代理校准。每种方法都与静态SHAP解释进行基准比较，评估指标包括预测性能（AUC、F1）、方向与排序稳定性（余弦相似度、Kendall tau）以及公平性（人口统计均等与重校准）。结果表明，自适应方法，尤其是重定基线和基于代理的解释，在显著提升时间稳定性的同时，减少了不同人口群体之间的差异性影响，且未降低预测准确性。包括反事实扰动、背景敏感性分析和代理变量检测在内的稳健性测试，证实了自适应解释在真实世界漂移条件下的韧性。这些发现确立了自适应可解释性作为一种实用机制，能够在数据驱动的信用系统中维持透明度、问责制和伦理可靠性，并且更广泛地适用于任何决策模型随群体变化而演化的领域。
英文摘要：
Evolving borrower behaviors, shifting economic conditions, and changing regulatory landscapes continuously reshape the data distributions underlying modern credit-scoring systems. Conventional explainability techniques, such as SHAP, assume static data and fixed background distributions, making their explanations unstable and potentially unfair when concept drift occurs. This study addresses that challenge by developing adaptive explanation frameworks that recalibrate interpretability and fairness in dynamically evolving credit models. Using a multi-year credit dataset, we integrate predictive modeling via XGBoost with three adaptive SHAP variants: (A) per-slice explanation reweighting that adjusts for feature distribution shifts, (B) drift-aware SHAP rebaselining with sliding-window background samples, and (C) online surrogate calibration using incremental Ridge regression. Each method is benchmarked against static SHAP explanations using metrics of predictive performance (AUC, F1), directional and rank stability (cosine, Kendall tau), and fairness (demographic parity and recalibration). Results show that adaptive methods, particularly rebaselined and surrogate-based explanations, substantially improve temporal stability and reduce disparate impact across demographic groups without degrading predictive accuracy. Robustness tests, including counterfactual perturbations, background sensitivity analysis, and proxy-variable detection, confirm the resilience of adaptive explanations under real-world drift conditions. These findings establish adaptive explainability as a practical mechanism for sustaining transparency, accountability, and ethical reliability in data-driven credit systems, and more broadly, in any domain where decision models evolve with population change.
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