ModelRefs / Interpretability — AI Glossary
Interpretability — AI Glossary
Methods for understanding the internal representations and decision processes of neural networks. Also called model interpretability or explainability.
Overview
Interpretability encompasses: saliency methods (which input tokens influenced the output), probing classifiers (what information is encoded in layer activations), circuit analysis (mechanistic interpretability), and concept activation vectors (TCAV). For LLMs: sparse autoencoders and activation patching are current frontiers in understanding transformer internals.
Reference details
| Topic | safety |
|---|---|
| Also known as | model interpretability, explainability |
| Last reviewed | 2026-06-24 |
Related terms
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Interpretability — AI Glossary.
Frequently asked questions
What is Interpretability?
Methods for understanding the internal representations and decision processes of neural networks.
Is Interpretability the same as model interpretability?
Yes — model interpretability, explainability are common aliases for Interpretability.
What concepts are related to Interpretability?
Closely related concepts include mechanistic interpretability, xai, shap.