用因果推断消除信贷评估中的替代数据偏见
Debiasing Alternative Data for Credit Underwriting Using Causal Inference
- 通过因果推断修正替代数据偏差，避免歧视性预测
- 在公开信用数据集上提升各族裔群体模型准确率
- 为金融机构提供合法合规的信贷评估新方法
替代数据为贷款机构评估借款人信用状况提供了宝贵信息，有助于扩大对弱势群体的信贷覆盖并降低借款人成本。但某些替代数据曾因可能作为种族或性别等受保护群体的非法代理而被排除在信贷评估之外，导致红线政策问题。本文提出一种将因果推断应用于监督学习模型的方法，以消除替代数据中的偏见，使其可用于信贷评估。我们在公开信用数据集上验证了该算法的有效性，结果表明其可在不牺牲性能的前提下，显著提升不同种族群体的模型准确率，并提供理论上可靠的非歧视保障。
原文摘要 · Abstract (English)
Alternative data provides valuable insights for lenders to evaluate a borrower's creditworthiness, which could help expand credit access to underserved groups and lower costs for borrowers. But some forms of alternative data have historically been excluded from credit underwriting because it could act as an illegal proxy for a protected class like race or gender, causing redlining. We propose a method for applying causal inference to a supervised machine learning model to debias alternative data so that it might be used for credit underwriting. We demonstrate how our algorithm can be used against a public credit dataset to improve model accuracy across different racial groups, while providing theoretically robust nondiscrimination guarantees.
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