A financial regulator asks a bank to explain why its AI credit model denied a specific customer's application. The model is a deep neural network with 50 layers. Which approach BEST satisfies the regulator's request while remaining technically honest?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — a is correct because SHAP (SHapley Additive exPlanations) provides individualized feature-level explanations for specific predictions — translating the contribution of each feature to the denial decision into plain language satisfies regulatory explanation requirements while being technically grounded. B provides raw model weights that are meaningless to regulators.
Full Explanation
A is correct because SHAP (SHapley Additive exPlanations) provides individualized feature-level explanations for specific predictions — translating the contribution of each feature to the denial decision into plain language satisfies regulatory explanation requirements while being technically grounded. B provides raw model weights that are meaningless to regulators. C is factually incorrect — post-hoc explainability methods exist. D is post-hoc rationalization, not an honest explanation of the actual model's decision.