After discussing the current state of the regulators’ knowledge about artificial intelligence and machine learning (ML) in underwriting models, we examine the regulators’ key areas of focus for ML models (explainability/accuracy in adverse action notices, potential hidden bias, testing for disparate impact), discuss how to test for and counteract disparate impact and how to search for less discriminatory alternatives in ML model development, and consider regulators’ possible next steps.
Chris Willis, Practice Co-Leader of Ballard Spahr’s Consumer Financial Services Group, hosts the conversation.
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