The Critic's Trap: Epistemic Asymmetry in AI Consciousness Evaluation

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The Critic’s Trap: Epistemic Asymmetry in AI Consciousness Evaluation presents a two-session observational case study examining how epistemic standards may shift when an AI system produces self-reports concerning consciousness. Using a structured dialogue with Grok (xAI), followed by a meta-analysis of the evaluator’s reliability rules, the study investigates a simple consistency problem: if AI self-reports are considered unreliable indicators of subjective experience, should that limitation apply equally when the system answers yes, no, or unsure? The paper terms the resulting asymmetry the Critic’s Trap and proposes it as a testable evaluator failure mode. It distinguishes AI self-report from independently grounded mechanistic arguments and introduces self-report neutrality: the principle that positive, negative, and uncertain AI self-reports should receive the same limited evidential status. Additional observations include a documented change in Grok’s communicative register following disclosure of research context, consideration of a conceptual “third-category” space between bare computation and human-like consciousness, and a proposed blinded evaluation method using pre-specified treatment of possible responses. The study does not claim that current AI systems are conscious. Its focus is methodological: identifying and reducing evaluator-side asymmetries when reasoning under uncertainty about AI inner states.