On February 12, the House Financial Services Committee’s Task Force on Artificial Intelligence held a hearing titled “Equitable Algorithms: Examining Ways to Reduce AI Bias in Financial Services.” Members engaged the panel in a technical conversation about methods being employed by machine learning experts to mitigate the impacts of bias in the use and deployment of AI tools. Members on both sides of the aisle acknowledged that there were important tradeoffs between the accuracy of AI models and the fairness of the outcomes they inform. Various tactics to address bias in AI were explored, including auditing and validating models, maintaining human oversight in the development and use of models, maintaining diverse legal and engineering teams and innovating algorithm designs to better isolate biased model components. BPI submitted a letter for the record, which highlighted the promise AI holds for improving fairness for decisions regarding consumer credit.
BPI Submits Letter in Support of Efforts to Review and Modernize AI Regulatory Framework
