# AI3.8 Explainable AI (XAI)

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.

## Requirements

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

## Learning Outcomes

* 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 **