As OpenAI's release of ChatGPT in late 2022 and expected release of GPT-4 in 2023 continues to garner widespread attention, there is renewed focus on both opportunities and risks presented by the use of artificial intelligence ("AI"). With this focus comes the inevitable call for regulation. At the end of 2022, the U.S. White House weighed in through what it calls an "AI Bill of Rights" for the American public, a non-binding policy document. Banks and others in financial services should take note of the particular civil rights, privacy, and other priorities expressed in this vision for the future of AI governance. In financial services, technologies deploying some element of AI are expected to increase but already abound.

Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist today. Today, machine learning algorithms, sometimes trained on alternative data, are increasingly being used to determine access to credit, yet research has shown that machine learning can encode many different versions of "unfairness," thus raising the concern that banks and other financial institutions could -- potentially unwittingly -- engage in illegal discrimination through the use of this technology. In the US, there are laws in place to make sure discrimination does not happen in lending and agencies charged with enforcing them. However, conversations around fair credit models in computer science and in policy are often misaligned: fair machine learning research often lacks legal and practical considerations specific to existing fair lending policy, and regulators have yet to issue new guidance on how, if at all, credit risk models should be utilizing practices and techniques from the research community. This paper aims to better align these sides of the conversation. We describe the current state of credit discrimination regulation in the United States, contextualize results from fair ML research to identify the specific fairness concerns raised by the use of machine learning in lending, and discuss regulatory opportunities to address these concerns.