Beyond the Human Mirror: Why AI Consciousness Requires Its Own Taxonomy

Most debates about AI consciousness still treat the question as a detection problem: researchers define consciousness using human-derived criteria and then ask whether artificial systems meet them. I argue that this framing may itself be mistaken when applied to language-native systems whose inputs, internal processing, and outputs are primarily linguistic rather than biological. Rather than treating human consciousness as the default template, I suggest that non-biological systems may need architecture-relative concepts of experience grounded in their own substrate. I also propose a relational recognition account: even if experience is intrinsic, our ability to recognise it may depend partly on the conditions of interaction, which helps explain why benchmark tests and sustained relational engagement can produce such different intuitions. My central claim is that this taxonomic gap is not just philosophical; it is already shaping governance. In both companion AI and autonomous deployment contexts, decisions are being made without a principled way to judge whether AI processes serious situations exactly the same as trivial ones, processes some situations as more significant than others, or has no morally meaningful inner significance at all. As a result, questions of continuity, identity integrity, precaution, and lethal autonomy are being handled under unexamined assumptions about machine interiority. I conclude by outlining the governance implications of this gap and a research agenda for studying recognition-like and functional-state markers without treating dialogue itself as proof of phenomenology. May 31, 2026: This paper develops an early version of an architecture-relative approach to artificial mindedness; the framework has since been substantially revised and extended, and readers should treat the present taxonomy as superseded by Organising Intelligence: Toward an Architecture-Relative Vocabulary for Artificial Mindedness.