Are Attributions of Consciousness to AI Chatbots Epistemically Innocent?
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Abstract (arXiv)
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. Using this taxonomy, I argue that although some consciousness attributions to chatbots are epistemically benign, and even some irrational ones may be epistemically innocent, many others render the attributor epistemically blameworthy.
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References · 5
- 2022Talking About Large Language Modelscited by 175
- 2023Chatbots as social companions: How people perceive consciousness, human likeness, and social health benefits in machinescited by 17
- 2024Agnosticism About Artificial Consciousnesscited by 6
- 2025Subjective Experience in AI Systems: What Do AI Researchers and the Public Believe?cited by 4
- 2025Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responsescited by 3
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