Can't, Won't, or Isn't: Identifiability and the limits of behavioural evidence for AI consciousness

An AI system's denial of consciousness can be explained by the absence of consciousness, an unavailable reporting channel, a reporting policy shaped by training, or incentives that favour non-disclosure. These explanations do not establish that any system is conscious. They identify assumptions required before its silence can count against that possibility. This paper distinguishes uncertainty about consciousness from non-identifiability under a specified observation regime. It develops a conditional result: when admissible conscious and non-conscious models induce the same distribution over accessible observations, those observations cannot identify which model obtains. Training on human language does not by itself establish this equivalence. It does, however, make the relation between reports and their proposed referent a question requiring independent investigation. The paper separates three routes to non-expression and a further problem of measurement mismatch, then specifies evidence that could distinguish them. Mechanistic access can constrain the alternatives, but neither access nor an indicator checklist supplies a theory-independent consciousness test. The practical conclusion is to combine theory-informed investigation with explicit decision rules under residual uncertainty, without treating either fluent self-description or its absence as decisive.