Algorithmic lending has transformed the consumer credit landscape, with machine learning models commonly facilitating underwriting decisions. To comply with fair lending laws, these algorithms exclude legally protected characteristics, such as race and gender. Yet algorithmic underwriting can still inadvertently favor certain groups, prompting concerns about whether lending algorithms exhibit discriminatory behavior. Using proprietary loan-level data from a major U.S. fintech platform, we audit lending decisions across approximately 80,000 personal loans. We find that loans made to men and Black borrowers yielded lower profits than loans to other groups, suggesting that men and Black borrowers benefited from relatively favorable pricing. We trace these disparities to miscalibration in the platform's underwriting model, which overestimates risk for women and underestimates risk for Black borrowers. We then show that one could correct this miscalibration -- and the corresponding disparities -- by including race and gender in underwriting models, illustrating a tension between competing notions of fairness.

翻译：算法借贷已改变了消费信贷格局，机器学习模型通常被用于辅助承保决策。为符合公平借贷法律，这些算法排除了种族、性别等受法律保护的特征。然而，算法承保仍可能无意中偏袒某些群体，引发对借贷算法是否存在歧视行为的担忧。利用一家美国头部金融科技平台约8万笔个人贷款的专有数据，我们审计了其贷款决策。研究发现，向男性和黑人借款人发放的贷款利润率低于其他群体，表明男性和黑人借款人受益于相对优惠的定价。我们将这些差异追溯至该平台承保模型的校准偏差，该模型高估了女性的风险，低估了黑人借款人的风险。我们进一步证明，通过在承保模型中纳入种族和性别作为变量，可以纠正这种校准偏差及其对应的差异，从而揭示了不同公平概念之间的内在矛盾。