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：受公平贷款法律约束的美国金融机构数十年来一直在运行算法公平性项目。尽管历史悠久，但这些要求在实践中如何运作的细节却鲜为人知。本文首次从实证角度揭示了金融机构如何在实地检测并缓解算法歧视。通过此举，我们阐明了公平贷款法律与法规的监管设计如何塑造了公平贷款项目的政策、流程与实践。基于对公平贷款生态系统中参与者进行的35次半结构化访谈，我们发现：虽然金融机构已建立一套旨在防止贷款歧视的公平实践底线（这在其他领域基本缺失），但各机构检测歧视及寻找更低歧视算法的方式差异显著。我们还发现，以公平贷款检查形式开展的监管监督是合规工作的关键驱动力，但公平贷款项目的实际效果往往取决于其能否有效协调相互冲突的商业激励、感知的法律张力及监管不确定性。最终，我们的研究凸显了监管权威在成功促进公平贷款实践中扮演的独特角色——这一监管设计特征既区别于民权法的其他领域，也几乎完全缺席于近期应对算法歧视的政策提案。