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Vice Chair Barr Speaks on Fair Lending in Age of AI   
Profile photo of contributor Daniel Meade
Partner | Financial Regulation
Profile photo of contributor Mercedes Kelley Tunstall
Partner | Financial Regulation

Michael Barr, the Federal Reserve Board’s (“FRB”) Vice Chair for Supervision, delivered remarks, titled “Furthering the Vision of the Fair Housing Act,“ to the National Fair Housing Alliance 2023 National Conference earlier this week. 

Vice Chair Barr noted the importance of the Fair Housing Act and the Equal Credit Opportunity Act (“ECOA”) “in making good on the promise of the Civil Rights Act, and of the vision of Dr. Martin Luther King, to build a fair and equal society.” He also noted that “today we can recognize the progress made in fair housing while also acknowledging how much farther we have to go and recommitting ourselves to reaching that destination.”

Vice Chair Barr went on to discuss the FRB’s role (along with the other federal banking regulators) in supervision and enforcement related to fair lending. He also noted that the Community Reinvestment Act (“CRA”) complements the fair lending laws to “address redlining and other systemic inequalities in access to credit, investment and banking services,” and that the agencies are hard at work finalizing a final CRA rule after the agencies issued a proposed CRA rule last May

The aspect of his remarks that may have garnered the most attention, however, was his comments about the importance of fair lending measures keeping up with technological change, such as artificial intelligence and machine learning (“AI”). Vice Chair Barr noted that “[w]hile these technologies have enormous potential, they also carry risks of violating fair lending laws and perpetuating the very disparities that they have the potential to address.” These comments echo statements from the Consumer Financial Protection Bureau, Federal Trade Commission, the Equal Employment Opportunity Commission and the Department of Justice earlier this year wherein they further explained that AI can lead to discriminatory results due to a variety of factors. These factors may include datasets used for training AI that are unrepresentative, imbalanced or that incorporate historical bias; the lack of transparency that AI systems have regarding how or why the systems make decisions; and the use of third-party AI solutions that have not been properly tailored or designed to accommodate fair lending considerations. 

Vice Chair Barr ended his speech by calling for the conference attendees “to remain ever vigilant against discrimination in credit transactions.” 

July 20, 2023
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