Fairness of Machine Learning in Credit Underwriting Models

Fairness of Machine Learning in Credit Underwriting Models

FinRegLab hosted a virtual conference on April 28, 2022, featuring research being conducted by FinRegLab 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 their potential implications for explainability and fairness. Serious questions exist about the ability of lenders to deploy machine learning underwriting models that meet anti-discrimination and equity expectations. The panel discussed concerns that the use of machine learning underwriting models will increase fair lending risks. Panel members also discussed specific approaches to improving the fairness of underwriting models, such as defining a framework for making fairness-performance tradeoffs and prospects for adversarial debiasing. Speakers Michael Akinwumi, Chief Tech Equity Officer, National Fair Housing Alliance Sri Satish Ambati, CEO and Co-Founder, H2O.ai Jay Budzik, CTO, Zest AI Nick Schmidt, CEO, SolasAI Moderator P-R Stark, Director of Machine Learning Research, FinRegLab