Both under-attributing and over-attributing consciousness to AI carry significant risks.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Underattribution of Consciousnessconcept0.813
  Risk of failing to identify consciousness in systems where it is present, potentially causing avoidable harm
• Overattribution of Consciousnessconcept0.810
  Risk of attributing consciousness to non-conscious systems, potentially wasting resources or endangering lives
• The risk of under-attribution of AI welfare appears to be both reasonably likely and reasonably harmful.claim0.803
  Key motivation for precautionary action.
• False negatives (ignoring genuine conscious experience in AI systems) carry potentially more severe risks than false positives, as they could constitute direct moral harm scaling with deployment and generate alignment risksclaim0.803
  Ethical argument motivating the research as a first-order priority
• Consciousness in AI is best assessed by drawing on neuroscientific theories of consciousness.claim0.801
  Central methodological claim of the paper.
• We hypothesize that as AI becomes more sophisticated and pervasive, more people will attribute consciousness to AI systems as a result of interactions.hypothesis0.801
  Predictive claim about the trajectory of public consciousness attribution as AI develops.
• The capacity for unlimited associative learning is not a good indicator for consciousness in AI.claim0.800
  Rejecting UAL as a reliable indicator for artificial systems.
• Acknowledgment of possible error about AI consciousness, combined with recognition of error costs, can motivate overlapping consensus on precautionary policies.claim0.796
  The error-sensitive model of overlapping consensus.