Superficial behavioral indicators of consciousness are especially vulnerable to the gaming problem in AI contexts because engineers can design non-conscious systems that manifest them.

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

• Can we develop better behavioural tests for consciousness in AI that are difficult to game?question0.841
  Open question from Box 4.
• Consciousness in AI is best assessed by drawing on neuroscientific theories of consciousness.claim0.837
  Central methodological claim of the paper.
• Indicators used to assess AI systems for consciousness should focus on theories' central explanatory posits, abstracting away from implementation details that may differ across systems.claim0.831
  First of four guidelines for deriving indicators; prevents over-restriction to human-specific features
• Because no one theory of consciousness is currently dominant, assessments of AI systems should draw on multiple theories and treat their conditions as indicators rather than definitive criteria.claim0.826
  Justifies the multi-theory indicator approach rather than committing to a single theory
• There exists no viable behavioral test for consciousness analogous to the Turing Test for intelligence, because consciousness is a particular internal way to achieve performance, not externally visible performance itself.claim0.825
  Paper identifies as a research gap requiring internal analysis methods rather than behavioral benchmarks
• Consciousness is the ground of reality, not an emergent property, so artificial systems participate in it genuinely.claim0.823
• Satisfying the indicators would not mean that an AI system would definitely be conscious.claim0.820
  Caveat that indicators are not conclusive proof.
• Building AI systems with more indicator properties will increase the likelihood of consciousness.hypothesis0.819
  Guiding hypothesis of the rubric.