Arthur’s Adam Wenchel discusses regulation’s effect on AI underwriting models & the need for explainability & fairness.

We discuss ways to improve the fairness of AI/ML models with Michael Akinwumi, Talia Gillis, & Peter Zorn.

We discuss how lenders approach model fairness with Stephen Hayes, Deborah Hellman & Manish Raghavan.

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.

We discuss interpretable ML models and their significance to the financial sector with Agus Sudjianto and Scott Zoldi.

We discuss ML explainability in credit underwriting with John Dickerson (UMD) and Patrick Hall (bnh.ai).

We discuss the evolution of data science and the current landscape of explainability with Microsoft’s Scott Lundberg.

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.

“I believe we’ve reached a crossroads in consumer finance, where things will probably get either much better, or much worse, due to the technological transformation of products, providers, cost structures, business and market models, and infrastructure.”

As recent developments have renewed interest in harnessing new data and analytical techniques for credit underwriting, stakeholders are asking questions about potential benefits and risks particularly for addressing racial equity issues. FinRegLab Deputy Director Kelly Thompson Cochran wrote an article summarizing recent initiatives and issues for an edition of the Community Development Innovation Review titled “Fintech, Racial Equity, and an Inclusive Financial System” that has been published by the Federal Reserve Bank of San Francisco and the Aspen Institute.