medium · FRM Part 2 Current Issues
When using LIME (Local Interpretable Model-agnostic Explanations) to explain a specific rejection by a deep-learning underwriting engine, a validator notes that the local linear surrogate model has a low R^2 in the neighborhood of the instance.
What is the primary risk associated with this finding?
- The model will be unable to produce any SHAP values for that instance due to the lack of local convergence.
- The bank is in violation of the Basel III Pillar 1 requirements for model-agnostic explainability.
- The local explanation is 'unfaithful' to the complex model and may provide misleading reason codes to the applicant.
- The underlying deep-learning model has likely overfitted the training data and should be retrained with more regularization.
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