Can interpretability reveal wrong goals?

Lens adapted to this topic: Limits of goal detection

This view argues that interpretability should not be mistaken for a dependable window into an AI’s goals. Human-readable features may be incomplete or ambiguous, the term interpretability itself lacks a settled definition, and even accurate internal representations may fail to support reliable correction. On this account, interpretability can provide clues, but strong claims about discovering true goals require causal validation and robust generalization.

• Mechanistic interpretability may rest on an overly strong assumption that tracing circuits will provide useful insight into behavior; critics report limited practical results so far.
• Interpretability is conceptually slippery, making it difficult to specify what counts as a successful explanation or to assess whether it solves the underlying safety problem.
• Even near-perfect internal representations may not translate into effective correction of model errors, illustrating a gap between knowing and changing what a model does.
• Internal probes and causal traces can identify correlated or influential representations without proving that they constitute the system’s stable, underlying goals.

Featured sourceAlignment Via Interpretability: Layerwise Counterfactuals To Detect Maladaptive Llm Behaviorsresearchsquare.com6 sourcesView sources