Explainability.

Explainability (or interpretability) is crucial for trust, debugging, and compliance. Methods include feature importance, attention visualization, LIME, SHAP, and concept activation vectors. There's often a trade-off between accuracy and explainability—simpler models are more interpretable but less powerful. Regulations increasingly require explainability in high-stakes decisions.