Propositional Interpretability in Artificial Intelligence

David J. Chalmers — 2025-01-27 — arXiv

Summary

Proposes propositional interpretability as a framework for mechanistic interpretability, arguing AI systems should be interpreted in terms of propositional attitudes (beliefs, desires, probabilities). Analyzes current interpretability methods (probing, SAEs, chain-of-thought) through this philosophical lens and introduces the challenge of ‘thought logging’.

Source

• Link: https://arxiv.org/abs/2501.15740
• Listed in the Shallow Review of Technical AI Safety 2025 under 1 agenda(s):
  • other-interpretability — White-box safety (i.e. Interpretability)

Related Pages

• other-interpretability