medium · Frm Part 2 Current Issues
A risk function is validating a complex Gradient Boosted Machine (GBM) model used for retail credit scoring. The developers have provided SHAP (Shapley Additive Explanations) values to meet regulatory adverse-action notice requirements.
Which of the following statements best describes a structural risk-management limitation of using post-hoc SHAP attributions compared to using an inherently interpretable model?
- SHAP values are global importance metrics and cannot provide the case-by-case 'reason codes' required by fair-lending regulations.
- The use of SHAP values inherently violates the monotonicity constraints required for conceptual soundness in credit scoring.
- SHAP values cannot handle non-linear feature interactions, making them less accurate than the GBM's internal importance metrics.
- SHAP attributions are approximations of the model's behavior and may exhibit instability across different feature resamples.
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