Several techniques are employed to make AI models explainable:
Feature Importance: This technique identifies which features (e.g., chemical properties) are most important in making predictions. In toxicology, this can help identify key factors contributing to toxicity. Saliency Maps: These visual representations highlight the areas of input data that are most influential in the AI model's decision-making process. Rule-Based Systems: These systems use a set of rules to explain the decision-making process. In toxicology, this can help elucidate the rules used to determine the toxicity of a substance. Model-Agnostic Methods: Techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) are used to explain the predictions of any AI model, regardless of its architecture.
