Behavioral Evidence is Still Insufficient to Identify Consciousness
Bibliographic record
Abstract
Researchers have started seriously considering the epistemic issue of whether and when we can claim an artificial intelli- gence (AI) has developed machine consciousness. Most cog- nitive theories of consciousness employ a functional character- ization of the property of consciousness. That is, they are com- mitted to an account of consciousness as a rule-governed pro- cess over mental states. Some cognitive scientists concerned with AI advocate an epistemically behaviorist approach to ma- chine consciousness; however, such approaches taken ontolog- ically, systematically fail to satisfy reasonable intuitions about in what consciousness ought to consist, and taken epistemi- cally, fail to provide sufficient evidence to individuate any in- ternal property, including consciousness, in non-human sub- jects. Therefore, in order to assess consciousness in ways that adequately account for reasonable intuitions as to its proper definition, such that we can reasonably assert the presence of machine consciousness in some AI, it is necessary to propose, test, and revise, functional theories of consciousness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".