Applying theories to AI through the indicator method may reveal hidden ambiguities or unintended implications, motivating advocates to clarify their views.

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• 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.claim0.827
  First of four guidelines for deriving indicators; prevents over-restriction to human-specific features
• The theory-derived indicator method provides a tractable way to reduce uncertainty about AI consciousness by deriving indicators from computational functionalist theories.claim0.825
  Central methodological claim of the paper
• Indicators are merely intended to be credence-shifting; we do not need to be certain that a theory is correct for it to provide useful indicators.claim0.819
  Key epistemic stance of the Bayesian approach to indicators
• Many of the indicator properties can be implemented in AI systems using current techniques.claim0.803
  Feasibility demonstrated in Section 3.1.
• The marker method can be adapted for AI systems by focusing less on behavioral evidence and more on architectural evidence.claim0.800
  Proposal for assessment framework.
• Indicator selection must avoid the minimal implementation problem while remaining open to varied forms of consciousness, including exotic AI forms.claim0.792
  Second guideline for deriving indicators; balance between demanding and permissive formulations
• There are no obvious technical barriers to building AI systems which satisfy the indicator properties.claim0.788
  Feasibility claim about near-term conscious AI.
• Building AI systems with more indicator properties will increase the likelihood of consciousness.hypothesis0.788
  Guiding hypothesis of the rubric.