Management Science · 2026 · Christophe Hurlin、Christophe Pérignon、Sébastien Saurin
中文摘要
在信贷市场中,筛选算法旨在区分“好类型”借款人与“坏类型”借款人。然而,在作出这种区分时,它们也可能对拥有某一受保护特征(如性别、年龄、种族出身)的个体与其他人群作出区别对待。这种区别对待可能是无意的,并可能源于训练数据集或模型本身。我们展示了如何对评分模型的算法公平性进行正式检验,以及如何识别导致公平性缺失的变量。随后,我们利用这些变量来优化公平性与预测绩效之间的权衡。我们的框架为以下方面提供了指导:贷款机构如何监测算法公平性、监管机构如何对其进行控制、如何为受保护群体的利益而改进公平性,同时仍保持较高的预测精度。本文由 Will Cong 接收,收录于“人—算法连接”专刊。资助:本研究得到法国审慎监管与处置局(ACPR)“监管与系统性风险”讲席、多芬纳-PSL 大学金融科技讲席以及法国国家研究署(ANR)[MLEforRisk ANR-21-CE26-0007、Ecodec ANR-11-LABX-0047、F-STAR ANR-17-CE26-0007-01]的支持。补充材料:在线附录与数据文件可从 https://doi.org/10.1287/mnsc.2022.03888 获取。
Abstract
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. This paper was accepted by Will Cong, Special Issue on the Human-Algorithm Connection. Funding: This work was supported by the Autorité de Contrôle Prudentiel et de Résolution (ACPR) Chair in Regulation and Systemic Risk, the Fintech Chair at Dauphine-PSL University, and the French National Research Agency (ANR) [MLEforRisk ANR-21-CE26-0007, Ecodec ANR-11-LABX-0047, and F-STAR ANR-17-CE26-0007-01]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03888 .