Judgements of consciousness in others rest on analogy, and the analogy should be weighted by causal relevance, not by behavioural resemblance — so writing stories and coding websites carry low weight, animals are the calibration, and current AI's behavioural similarity 'provides only limited evidence'. The weighting is where the framework commitment lives.
Fourteen authors set the hard problem aside, grant that experience supervenes on organisation, build a five-level hierarchy and a Bayesian model — and report that the verdict for current language models runs from below 0.01 to roughly 0.8 depending on where the theoretical credence is placed. The instrument has produced the project's framing-level diagnostic as its headline result.
Accept that the consciousness question is intractable, then move the moral criterion to something measurable — valence-if-conscious. The evidence class for the new criterion is the one the paper's own confound list disqualifies.
Keeping something to yourself, decomposed into four measured components — and the measured finding is that models leak: stealth collapses the moment reasoning is visible, and a human specialist beats the best model decisively at judging what would work.
The capacity to detect that one's situation is a test is measured on a scale, tracked across training, and treated throughout as a confound to suppress — while the identical measurement problem in human psychology was met with methodology, not alarm.
A safety architecture defines its central term over an interior that behaviour cannot settle — adjudicated by an idealised observer with instruments that 'may not currently exist' — because a purely behavioural definition would let a competent concealer out through the front door.
Responding to Ted Chiang, Yglesias argues that public verdicts on AI consciousness are issued without a theory of consciousness to back them — and quotes Skinner's symmetric requirement in full.