U.S. financial institutions subject to fair lending laws have been running algorithmic fairness programs for decades. Despite this long history, remarkably little is known about how these requirements operate in practice. In this paper, we offer the first empirical account of how financial institutions test for and mitigate algorithmic discrimination on the ground. In doing so, we shed light on how the regulatory design of fair lending law and regulation have shaped the policies, processes, and practices of fair lending programs. Drawing on 35 semi-structured interviews with participants across the fair lending ecosystem, we find that while financial institutions have a floor of fairness practices aimed at preventing discrimination in lending largely absent in other domains, the specifics of how firms test for discrimination and search for less discriminatory algorithms varies widely. We also find that regulatory supervision via fair lending examinations has been the key driver of compliance work, but that the practical impact of fair lending programs often depends on how well they can navigate competing business incentives, perceived legal tensions, and regulatory uncertainty. Ultimately, our findings highlight the unique role that supervisory authority has played in successfully fostering fair lending practices -- a regulatory design feature that is distinct from other areas of civil rights law and almost completely absent from recent policy proposals for dealing with algorithmic discrimination.

翻译：受公平借贷法约束的美国金融机构已实施算法公平性项目数十年。尽管历史久远，但学界对这些要求如何在实践中运作知之甚少。本文首次从实证角度揭示金融机构如何在实地检测并缓解算法歧视，进而阐明公平借贷法律与监管的制度设计如何塑造公平借贷项目的政策、流程与实践。通过对公平借贷生态系统中35位参与者的半结构化访谈，本研究发现：虽然金融机构已建立多数领域缺失的最低限度公平实践框架以防止借贷歧视，但各机构检测歧视及搜寻低歧视性算法的具体方式存在显著差异。研究还发现，公平借贷检查的监管监督是合规工作的核心驱动力，但公平借贷项目的实际效果往往取决于其能否妥善协调商业激励冲突、法律张力感知与监管不确定性。最终，我们的研究凸显监管权威在成功培育公平借贷实践中的独特作用——这一制度设计特征不仅区别于其他民权法领域，更几乎完全缺失于当前应对算法歧视的政策提案中。