When should we trust a latent representation?

In a post on LessWrong, Ratnaditya J argues that just because a latent representation in an AI model can causally influence behavior doesn't necessarily mean we correctly understand what that representation actually does. For example, a safety monitor might detect a signal linked to malicious cyber planning, but that same signal could activate for benign activities like vulnerability research or coding optimization. The original causal evidence remains valid, but our semantic interpretation—what the signal actually means—becomes uncertain. This matters for deployment: would you use a safety signal you don't fully trust? Rather than yet another interpretability metric, the post proposes Evidence Profiles, a framework that explicitly maps accumulated evidence to semantic claims, treating interpretations as claims calibrated to evidence strength rather than binary conclusions. The argument suggests this gap between causal proof and semantic trust is a real barrier to translating safety research into deployable systems.

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