提出新方法检测信贷模型对不同人群的决策逻辑差异
Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions
- 用反事实解释一致性检测模型是否对不同群体用相同推理方式
- 实验证明现有公平模型仍存在隐藏的决策逻辑偏差
- 适合关注算法公平性深层问题的研究者和从业者
在社会敏感领域（如信贷决策）中，机器学习模型常聚焦于均衡预测结果。然而，满足这些指标并不保证模型对不同群体采用相同的推理方式。我们发现，现有基于结果公平的模型可能对不同个体使用根本不同的推理逻辑，这种‘隐藏的过程偏见’被标准公平度量和算法所忽略。为此，我们提出反事实解释一致性（CEC）框架，通过对齐个体与其反事实样本的特征归因来检测和缓解此类偏差。关键贡献包括一种最近邻反事实生成方法、改进的积分梯度基线、个体层面的过程公平度量及相应训练损失。我们提出分类体系，识别出‘制度B’（相同结果但不同推理）为重要盲点。在合成数据、德国信贷、Adult收入和HMDA抵押贷款数据上的实验表明，结果公平基线存在显著隐藏偏差，而CEC可有效降低该偏差，且仅带来适度性能损失。
原文摘要 · Abstract (English)
Machine learning algorithms in socially sensitive domains (e.g., credit decisions) often focus on equalizing predictive outcomes. However, satisfying these metrics does not guarantee that models use the same reasoning for different groups. We show that existing outcome-fair models can still apply fundamentally different reasoning to individuals, a ``hidden procedural bias'' missed by standard fairness metrics and algorithms. We propose Counterfactual Explanation Consistency (CEC), a framework that detects and mitigates this bias by aligning feature attributions between individuals and their counterfactual counterparts. Key contributions include a nearest-neighbor counterfactual generation method, a modified baseline for integrated gradient comparisons, an individual-level procedural fairness metric, and a corresponding training loss. We introduce a taxonomy identifying ``Regime B'' (same outcome, different reasoning) as a critical blind spot. Experiments on synthetic data, German Credit, Adult Income, and HMDA mortgage data demonstrate that outcome-fair baselines exhibit substantial hidden bias, while CEC substantially reduces it with modest utility cost.
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