scientific-explainable-ai

You can explain model predictions using SHAP and LIME by generating global summaries for overall feature importance and local explanations to justify individual decisions. This approach quantifies model behavior and visualizes the reasoning behind specific outputs.

Global explanations in explainable AI reveal overall model behavior and feature importance across datasets, while local explanations justify individual predictions. Both utilize SHAP and LIME to quantify and visualize the specific decision logic of machine learning models.

You perform a fairness audit and counterfactual analysis to assess model robustness and regulatory readiness. This process evaluates decision logic across different groups and generates counterfactual scenarios to identify potential biases in scientific data workflows.

Yes, the explainable AI pipeline supports both model-agnostic and model-specific methods for scientific data tasks. It utilizes tools like SHAP, LIME, Captum, and InterpretML to deliver modular XAI workflows with emphasis on safety and regulatory alignment.

You generate audit-ready reports for regulatory compliance by running the XAI pipeline to produce global SHAP summaries, local explanations, counterfactual analyses, and fairness diagnostics. This ensures scientific workflows meet safety standards and regulatory alignment requirements.