ds-model-explainability

Explain ML model predictions for regulatory reviews by generating global summaries, local explanations, and regulatory artifacts using SHAP, LIME, and fairness metrics to audit biases and document model behavior for stakeholders.

Yes, you can generate model cards to audit fairness for deployment documentation by calculating fairness metrics and documenting model behavior across tabular, image, and text data to communicate predictions and biases to stakeholders.

Global interpretability provides overall feature importance summaries using PDP/ICE curves, while local interpretability explains individual predictions using SHAP and LIME values to clarify specific outcomes for stakeholders.

Yes, it works for explaining predictions on text and image models by applying modular explanation toolkits like SHAP and LIME to generate global summaries and local explanations across tabular, image, and text modalities.

Use counterfactuals and PDP/ICE for model explainability to generate global summaries showing feature impacts and local explanations identifying alternative outcomes, enabling stakeholder communication and ongoing monitoring.