The Imitation Fallacy: Why Behavioral Equivalence Cannot Verify Artificial Consciousness

Abstract

Modern language models and multimodal agents routinely pass behavioral benchmarks that once served as proxies for ‘thinking.’ Popular discourse increasingly treats these performances as evidence of consciousness. We argue that this inference commits the Imitation Fallacy: confusing behavioral equivalence with experiential equivalence. Building on the Subjectivity Barrier, the epistemic fact that consciousness is irreducibly first person we show that even a perfect Turing style performance leaves an agent’s inner life forever uncertain. As a result, no external test, however sophisticated, can verify or falsify artificial consciousness. We formalize this limit, derive the Undetectability Corollary, and propose the Emergence Criterion as a more principled though still probabilistic framework for assessing when artificial systems might deserve moral consideration. Our analysis reframes ongoing debates about AGI, rights, and AI safety and clarifies why ‘human level" intelligence is an incoherent benchmark for consciousness evaluation.This paper presents a conceptual and theoretical contribution aimed at clarifying a cognitive error in AI perception. We do not conduct new empirical work, but instead synthesize insights from cognitive science and public behavior to outline a formal framework for evaluating consciousness claims in artificial agents.