Press Releases

FinRegLab: Symposium–“Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI”

Joint event featuring prominent policymakers to focus on responsible AI in financial services.


FinRegLab, in partnership with the U.S. Department of Commerce, the National Institute of Standards and Technology (NIST), and the Stanford Institute for Human-Centered Artificial Intelligence (HAI), will host a symposium on April 27, 2022, bringing together leaders from government, industry, civil society, and academia to explore potential opportunities and challenges posed by artificial intelligence and machine learning deployment across different economic sectors, with a particular focus on financial services and healthcare.

Confirmed speakers include Don Graves, Deputy Secretary of Commerce; Joni Ernst, Senator; Michael Hsu, Acting Comptroller of the Currency; JoAnn Stonier, Executive Vice President and Chief Data Officer at Mastercard; Agus Sudjianto, Executive Vice President and Head of Model Risk at Wells Fargo; Dr. Susan Athey, Professor at Stanford Graduate School of Business and Associate Director of HAI; Dr. Nicol Turner Lee, Director, The Center for Technology Innovation, The Brookings Institution; and Dr. Manish Raghavan, Postdoctoral Fellow, the Harvard Center for Research on Computation and Society. Speakers and panelists will discuss research, policy recommendations, and emerging industry practices.

“AI, when combined with new types of data, presents enormous potential to improve financial inclusion and equality,” says Melissa Koide CEO and Director of FinRegLab. “However, there is also enormous risk of deepening bias and exclusion. Careful, specific research to understand how consumers are affected is essential to getting the rules of the road right.”

FinRegLab will also host a virtual conference on April 28, focusing in greater detail on the research conducted by the organization and Professors Laura Blattner and Jann Spiess of the Stanford Graduate School of Business on the use of machine learning in credit underwriting, with a particular focus on the potential implications of machine learning models for explainability and fairness. This research empirically evaluates the performance and capabilities of currently available tools designed to help lenders develop, monitor, and manage machine learning underwriting models.

Members of the press interested in attending the symposium in-person or virtually or seeking a comment should contact Alex Bloomfield at For more information on the symposium, including all presenters and panel discussions, please visit the event page here.

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Related Publications

  • Machine Learning Explainability & Fairness: Insights from Consumer Lending

    This empirical white paper assesses the capabilities and limitations of available model diagnostic tools in helping lenders manage machine learning underwriting models. It focuses on the tools’ production of information relevant to adverse action, fair lending, and model risk management requirements.


  • Explainability and Fairness in Machine Learning for Credit Underwriting

    FinRegLab worked with a team of researchers from the Stanford Graduate School of Business to evaluate the explainability and fairness of machine learning for credit underwriting. We focused on measuring the ability of currently available model diagnostic tools to provide information about the performance and capabilities of machine learning underwriting models. This research helps stakeholders… Learn More

About FinregLab

FinRegLab is an independent, nonprofit organization that conducts research and experiments with new technologies and data to drive the financial sector toward a responsible and inclusive marketplace. The organization also facilitates discourse across the financial ecosystem to inform public policy and market practices. To receive periodic updates on the latest research, subscribe to FRL’s newsletter and visit Follow FinRegLab on LinkedIn and Twitter (X). | 1701 K Street Northwest, Suite 1150, Washington, DC 20006