FinRegLab 2022 - Explainability of Machine Learning in Credit Underwriting Models

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The complexity of models derived by AI/ML algorithms poses fundamental challenges for oversight: How can we gain sufficient insight into how a model makes predictions in order for model users and their regulators to enable oversight and governance? The panel discussed debates over the use of inherently interpretable underwriting models as compared to post hoc diagnostic tools.  The panel also considered  which explainability challenges and limitations will most likely be the focus for practitioners over the next decade.

Laura Kornhauser, Co-founder and CEO, Stratyfy
Scott Zoldi, Chief Analytics Officer, FICO
Krishnaram Kenthapadi, Chief Scientist, Fiddler
Adam Wenchel, CEO, Arthur
Molham Aref, CEO,

Moderator: Jann Spiess, Assistant Professor of Operations, Information & Technology, Stanford Graduate School of Business

Video transcript