All glossary terms
conceptsadvanced
Interpretability
The ability to understand and explain AI model decisions and internal representations.
In more detail
Interpretability methods include attention visualization, saliency maps, feature importance, and mechanistic interpretability. Critical for trust, debugging, and regulatory compliance. Active research area with challenges.
Examples
- Attention visualization in Transformers
- Feature importance in tree models
- Saliency maps for images
- SHAP explanations
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