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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.
We discuss ML explainability in credit underwriting with John Dickerson (UMD) and Patrick Hall (bnh.ai).
We discuss interpretable ML models and their significance to the financial sector with Agus Sudjianto and Scott Zoldi.
We discuss the evolution of data science and the current landscape of explainability with Microsoft’s Scott Lundberg.
The podcast currently has 6 episodes available.