FinRegLab and Professors Laura Blattner and Jann Spiess of the Stanford Graduate School of Business have released new research on “Machine Learning Explainability and Fairness: Insights from Consumer Lending.”

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), are hosting a symposium, 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.

Coordinated action is critical between federal regulators to continue moving the growing ecosystem for customer-directed transfers toward adoption of safer technologies and practices without undermining consumers’ § 1033 rights or frustrating the law’s potential benefits for competition and innovation.

FinRegLab Deputy Director Kelly Thompson Cochran testified in the Task Force’s hearing on “Preserving the Right of Consumers to Access Personal Financial Data.”

Machine learning models are being used to evaluate the creditworthiness of tens of thousands of consumers and small business owners each week in the U.S., increasing the urgency of answering key questions about their performance, governance, and regulation.

The Board of Governors of the Federal Reserve System, the Federal Deposit Insurance Corporation, and the Office of the Comptroller of the Currency released a guide for community banks on conducting diligence of financial technology companies. Drawing on existing guidance that articulates risk management expectations for third-party relationships, the guide highlights areas where required diligence processes can be adapted to reflect constraints related to doing business with early or expansion stage companies.

FinRegLab’s forthcoming research will help to inform the extent to which current laws and regulations are able to be satisfied in light of the emergence of more complex underwriting models, how well tools to develop and monitor those models perform in identifying effective ways to pursue greater inclusion and fairness, and considerations for policy and market developments that can support the safe, inclusive, and nondiscriminatory adoption of machine learning.

FinRegLab CEO Melissa Koide testified in the Task Force’s hearing on “Equitable Algorithms: How Human-Centered AI Can Address Systemic Racism and Racial Justice in Housing and Financial Services.”

FinRegLab is working with researchers from Stanford Graduate School of Business to launch a ground-breaking evaluation of emerging market practices to improve the transparency and fairness of machine learning underwriting models in consumer credit.

We recognize the breadth of urgent issues facing the Consumer Financial Protection Bureau and the nation at this time, but believe that resolving critical questions about access to financial data would substantially benefit consumers, small businesses, and financial services providers in helping to recover from the Covid-19 pandemic, address longstanding racial wealth gaps, and make U.S. financial systems more generally inclusive, competitive, and responsive to customer needs.