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

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• 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.
• 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.
• How does the individuation of AI systems affect consciousness attributions?question0.786
  Open question about copying and distributed systems.
• Consciousness (ability to suffer) should not be assigned only to one singular 'I' but distributed across multiscale body components.claim0.779
• Given our body’s composition as a multi-scale system, it is not necessarily justified to assign consciousness (i.e., ability to suffer) only to one single ‘I’.claim0.775
  Argues for distributed consciousness, supporting extension of care.
• AI systems which possess more of the indicator properties are more likely to be conscious.claim0.775
  Graded claim about the rubric.