Summary:
- The article explores practical strategies for improving the transparency and explainability of machine learning models.
- It details three specific methodologies—feature importance analysis, partial dependence plots, and surrogate models—to help practitioners understand how AI makes decisions.
- The content focuses on the technical necessity of interpretability for debugging, model auditing, and building trust in automated systems.