Abstract

Recent debates over artificial intelligence and consciousness have focused primarily on whether current or future AI systems might possess sentience, subjective experience, self-awareness, or moral status. This paper argues that these debates have an underexplored reflexive significance: in attempting to determine whether artificial systems could be conscious, humans are forced to clarify the evidential and conceptual criteria by which consciousness is recognized at all. Drawing on contemporary work in AI consciousness, philosophy of mind, cognitive science, and anthropomorphism studies, the paper argues that AI functions as a conceptual mirror for human self-understanding. Large language models and related systems destabilize familiar consciousness-attribution practices because they display fluent linguistic self-presentation without biological embodiment, organic vulnerability, or ordinary developmental history. This dissociation exposes a latent asymmetry in human judgments: human self-reports of experience are often treated as presumptively meaningful, while AI self-reports are typically discounted as mimicry, role play, or training-data residue. The paper formalizes this asymmetry through a consciousness-attribution model that distinguishes observable evidence, prior assumptions, interpretive weights, and moral risk. It then proposes a reflexive feedback model in which attempts to operationalize AI consciousness reshape human concepts of consciousness themselves. The resulting argument is not that current AI systems are conscious, but that AI-consciousness debates reveal the unsettled foundations of human consciousness attribution and thereby deepen the philosophical study of human minds.