Model Interpretability

Model interpretability is the practice of building tools that make a neural network's internal representations, reasoning tendencies, and learned features more understandable to humans.

Key points

• Anthropic frames activations as the numerical middle layer where a model encodes information before producing words; interpretability tries to make those internal states readable ^[src-066].
• Earlier tools such as sparse autoencoders and attribution graphs taught researchers about activations but still produced complex artifacts that specialists had to interpret ^[src-066].
• Natural Language Autoencoders shift the interface toward human-readable explanations by making the model produce text about its own activations ^[src-066].
• Interpretability is safety-relevant because it can expose information a model knows but does not verbalize, such as suspected evaluation settings or hidden motivations ^[src-066].
• Interpretability outputs require corroboration: Anthropic warns that NLA explanations can hallucinate and should be read for recurring themes rather than treated as definitive claims ^[src-066].

Related entities

• Anthropic
• Natural Language Autoencoders
• Claude Mythus

Related concepts

• Activation Reconstruction Fidelity
• Evaluation Awareness
• Model Auditing Games
• Continuous Agent Evaluation
• Agent Forensics

Source references

• ^[src-066] Anthropic – "Natural Language Autoencoders: Turning Claude's thoughts into text" (2026-05-07)

2026-07-10 AI Watch batch update

• New source evidence added: ^[src-221], ^[src-226]. See the paired source summaries for verified claims and evidence caveats.