The Fairness of Credit Scoring Models

• 成果类型: Article; Early Access
• 署名作者: Hurlin, Christophe; Perignon, Christophe; Saurin, Sebastien
• 署名单位: Universite de Orleans; Institut Universitaire de France; Hautes Etudes Commerciales (HEC) Paris
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.03888
• 发表日期: 2024
• 关键词: credit markets; DISCRIMINATION; Machine Learning; Artificial intelligence

摘要

In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g., gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training data set or from the model itself. We show how to formally test the algorithmic fairness of scoring models and how to identify the variables responsible for any lack of fairness. We then use these variables to optimize the fairness-performance tradeoff. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, improved for the benefit of protected groups, while still maintaining a high level of forecasting accuracy.