person Author: Process Fellows

1. Post-hoc explanations (explanations after the decision): Already trained model is analyzed to explain why it made a certain decision.
   • Saliency maps - Shows which input features were important (e.g. for images).
   • LIME (Local Interpretable Model-agnostic Explanations) - Creates simplified, linear models to explain individual predictions.
   • SHAP (Shapley Additive Explanations) - Calculates the influence of each input feature on the prediction.
   • Counterfactual Explanations - Shows how the decision would change if certain inputs were different.
2. Intrinsically explainable models: Models are designed to be explainable from the outset, examples:
   • Decision trees - Clear “if-then” rules.
   • Linear regression - Direct correlation between input and output.