Several techniques are employed to make AI models explainable: 1. Feature Importance: Determines which input features are most influential in the model’s predictions. 2. Partial Dependence Plots: Visualizes the relationship between a feature and the predicted outcome. 3. Surrogate Models: Simplified models that approximate the behavior of more complex models. 4. SHAP Values: Quantify the contribution of each feature to the final prediction. 5. LIME (Local Interpretable Model-Agnostic Explanations): Provides interpretable explanations for individual predictions.
