shap-explainer

Explain machine learning model predictions for regulatory compliance by calculating SHAP values to quantify local and global feature importance, satisfying transparency requirements like the EU AI Act's Article 13.

Visualize feature importance across an entire dataset by generating global interpretability plots and dependency plots using SHAP values, which highlight the most influential features and their relationships to the model output.

Identify which features influenced a specific loan default risk prediction by applying local interpretability with SHAP values, highlighting key factors like credit score or debt-to-income ratio for that individual decision.

You need a trained machine learning model and a specific data point to generate SHAP values for interpretation, as the analysis relies on evaluating how features impact the model's actual output.

This approach supports both local and global interpretability for AI models by leveraging SHAP values to explain individual predictions and identify overarching feature importance across the full dataset.

A limitation of using SHAP values for model interpretation is that it requires integrating with third-party SHAP libraries to perform detailed analysis, adding a dependency to the workflow.