This skill introduces methods and frameworks that enable interpretability of AI models, especially in high-stakes or scientific contexts. It emphasizes tools, techniques, and best practices for understanding, auditing, and communicating model behavior.

• External: Understanding of basic AI model structure and outputs
• Internal: None

• Define explainability and distinguish it from transparency and interpretability.
• Identify common XAI methods (e.g., SHAP, LIME, saliency maps) and their applications.
• Apply interpretability techniques to evaluate model decisions in classification or regression tasks.
• Describe use cases for explainability in scientific research, safety-critical systems, and compliance.
• Evaluate trade-offs between model complexity and interpretability.

Caution: All text is AI generated