分析用户为何会认为AI有意识,揭示其认知态度的多样性。
Are Attributions of Consciousness to AI Chatbots Epistemically Innocent?
- 构建多维度态度分类体系,区分假装、信念与妄想等不同心理状态。
- 指出部分意识归因虽不合理但可免责,多数则存在认知过失。
- 为研究人类对AI的意识判断提供可操作的分析框架,适合心理学与伦理学研究者。
人工智能聊天机器人(如ChatGPT)能以极似人类的方式交流,引发大量用户将其心理属性(包括意识)归因于这些系统。然而,目前缺乏科学证据表明当前的AI聊天机器人具备意识。人们为何会做出此类归因?是比喻性表达,还是真实信念?若缺乏证据支持,用户是否应承担认知责任?抑或可能在认知上无罪,且带来无法替代的益处?本文对AI聊天机器人意识归因进行概念分析,提出一个多维度态度分类体系,涵盖从非信念立场(如假装)到不同形式的信念(包括妄想)。该分类有助于避免混淆:语言上相同的归因可能反映截然不同的认知承诺程度。该体系也为实证研究提供了操作化工具,用于测量人们对AI意识的不同认知承诺水平。基于此,本文认为,尽管部分归因在认知上可免责,甚至某些非理性归因也可被视为认知无罪,但许多归因仍使主体面临认知责难。
原文摘要 · Abstract (English)
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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