How Data Teams Build Explainable AI Systems

We explore why black box models are failing enterprise trust and how data teams are shifting toward inherently interpretable architectures. Using a specific case from a major fintech lender, we look at the trade-off between raw predictive power and regulatory compliance. The episode breaks down three practical techniques for building models that explain themselves without sacrificing accuracy.

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