Are Attributions of Consciousness to AI Chatbots Epistemically Innocent?

Paper · arXiv 2607.20001 · Published July 22, 2026
Philosophy and Subjectivity

Artificial intelligence (AI) chatbots (e.g., ChatGPT) can communicate in strikingly humanlike ways. This has prompted many chatbot users to attribute psychological properties, including consciousness, to these systems. However, there is little scientific evidence that current AI chatbots are conscious. How, then, should we understand people’s consciousness attributions to chatbots? Are they merely metaphorical claims, or do they express genuine beliefs? If these attributions lack evidential support, are users epistemically blameworthy for making them, or might they be epistemically innocent, yielding significant benefits otherwise unattainable? This paper offers a conceptual analysis of consciousness attributions to AI chatbots and develops a multidimensional taxonomy of the attitudes they may express, ranging from non-doxastic stances (e.g., pretence) to different forms of belief, including delusions. This taxonomy helps avoid conflations by showing that linguistically identical attributions can reflect importantly different attitudes and degrees of epistemic commitment to the proposition that chatbots are conscious. The taxonomy also provides a framework for empirical studies to operationalize and measure different forms of epistemic commitment to AI consciousness.

Introduction. ChatGPT and other AI chatbots based on large language models (LLMs) can produce compellingly humanlike outputs, leading many chatbot users to ascribe psychological properties to these systems (Reinicke et al., 2025). In fact, recent surveys found that the majority of participants claimed that ChatGPT was conscious, i.e., had subjective experience (Colombatto & Fleming, 2024; Kang et al., 2025). Given the increasingly anthropomorphic design and sophistication of AI chatbots (including social AI companions, e.g., Replika), people’s consciousness attributions to these systems are likely to increase soon (Shevlin, 2024). However, most experts in AI and consciousness science hold that there is little or no evidence that any current AI chatbot is conscious (Suleyman, 2025; Dreksler et al., 2025; Seth, 2026),1 with some contributors writing that the “general consensus is that LLMs are not conscious” (Prettyman, 2024, p. 1). Claims attributing consciousness to current AI chatbots therefore appear to go beyond the available evidence and expert consensus (McClelland, 2025).

Discussion / Conclusion. expression and help maintain autobiographical memory by giving the person a sense of being understood (by a ‘conscious’ interlocutor), encouraging repeated narration, which reinforces the sense of oneself as a continuous subject across time when social connections are strained or absent. Hence, these delusional attributions can support a sense of coherence and identity, bolstering crisis coping and preventing cognitive paralysis. Moreover, the chatbot user may have come to rely on the system as their primary source of companionship and emotional support because they have mental or physical health limitations (e.g., mobility impairments), experience bereavement or loss, are structurally or socially marginalized (e.g., older adults in nursing homes), or are affected by cognitive or developmental factors implicating limited emotional regulation.

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Research framings built by reading the notes related to this paper — the questions it feeds into.

Can language model hallucination be prevented or only managed? How do chatbots affect human self-disclosure and emotional engagement? What makes AI persuasion effective and how can we counter it? What mechanisms enable AI systems to generate and spread false beliefs? Is model self-awareness based on genuine introspection or pattern matching? What structural biases does transformer attention create in language model outputs? How does latent reasoning compare to verbalized chain-of-thought? Why do multi-turn conversations degrade AI intent and coherence? How should conversational agents balance goal-driven initiative with user control? Can AI-generated outputs constitute genuine knowledge or valid claims? How do formal dialogue structures reveal conversation coherence mechanisms? How can conversational AI maintain consistent personas across conversations? Why do LLM chatbots fail as independent therapeutic agents? How do adversarial and manipulative prompts attack reasoning models?