Evaluate Model Interpretability with SHAP

Table of Contents

1. Challenge

   Understand How to Prepare a Registered Model and Dataset for SHAP Interpretability

   • Connect to the Azure ML workspace used by your credit risk team.
   • Create and register a credit risk classification model in MLflow format.
   • Create and register training and test datasets in MLTable format.
   • Review how the model and datasets are linked within Azure ML for reproducible analysis.
2. Challenge

   Demonstrate How to Generate and Explore Global and Local SHAP Explanations

   • Configure and run a SHAP-based explainer using the Responsible AI dashboard.
   • Review global explanations to identify the top features driving default risk.
   • Explore local explanations for specific applicants and interpret how SHAP values combine into a prediction.
   • Walk through one high-risk applicant and justify the prediction using SHAP evidence.
3. Challenge

   Demonstrate How to Communicate Model Behavior and Risks Using SHAP Outputs

   • Translate SHAP charts and values into plain language statements for non-technical stakeholders.
   • Document key drivers, example applicant explanations, and any concerns in a governance-ready summary.
   • Explain how SHAP interpretability supports responsible AI in regulated banking use cases.
   • Draft a short note to the risk committee on how the model works, what matters most, and how to monitor these drivers over time.