研究：Chain-of-Thought 推理展示加剧拟人化与认知依赖风险
When Reasoning looks like Hidden Understanding: How Chain-of-Thought Fosters Anthropomorphism, Perceptions of Epistemic Superiority, and Cognitive Deference
这篇论文把 CoT 当成可解释性工具这件事直接提出了质疑：展示推理过程反而让用户更盲信模型。拿医疗决策举例很具体，做 AI 产品和做 AI 素养培训的人都该看看。
一篇 arXiv 论文指出，Chain-of-Thought（CoT）这类模型输出的推理过程常被用户误当作模型真正“理解”的证据，而生成式模型实际只是在复现语言模式。论文将这种误解归纳为拟人化与“认知权威感”的组合：用户把模型当作有意图的“心智”，进而产生认知依赖（cognitive deference），把局部任务表现泛化为全面能力，犯下“合成谬误”。
作者以医疗场景为例说明后果：医生若基于模型展示的 CoT 理由做出诊断决策，可能让渡本应由人承担的判断责任。论文结论是，通过 CoT 实现的“可解释性”存在问题，甚至与其想服务的伦理原则相悖。作者承认 CoT 在提升输出准确度和内容审核上有价值，但建议主动劝阻让模型向用户展示推理理由的做法，并在 AI 素养培训中明确提示这一风险。
When Reasoning looks like Hidden Understanding: How Chain-of-Thought Fosters Anthropomorphism, Perceptions of Epistemic Superiority, and Cognitive Deference
As AI-based products permeate society, specifically systems using generative models, a belief system is emerging about what these products are and what they can and cannot do. One common belief concerns the perception of the form of language as evidence of 'understanding' language. As these generative models reproduce linguistic patterns rather than understand meaning, interpreting these patterns as evidence of understanding is a misattribution. This misconception is extended by the notion that generative models can 'explain their reasoning.' Anecdotally, these misconceptions which mistake the form of language for its meaning are driving cognitive deference to AI systems in a way that displaces human agency. Such mistakes involve attributions of cognitive properties to the system, allowing them to be treated like intentional 'minds'. Importantly, perceived epistemic authority and superiority make that 'mind' worth deferring to. Anthropomorphism in tandem with epistemic authority and superiority, we argue, increases the risk that users generalise from narrow task performance to broader competencies, committing the "fallacy of composition". This paper explores misconceptions of Chain of Thought (CoT) reasoning as sufficient transparent 'explanation'. We explore how anthropomorphism acts as a vehicle for cognitive deference of decision-making and perceptions of epistemic superiority in AI systems. Finally, we explore CoT's ramifications using medicine as an example. Taken together, we conclude that explainability operationalised through CoT is highly problematic and undercuts the very ethical principles it is intended to serve. While CoT may be valuable for improving output accuracy and moderation, we suggest that the practice of asking models to provide user facing reasons for their outputs should be actively discouraged, and AI literacy training should explicitly caution against its use.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Chain-of-Thought、可解释性、AI安全、认知依赖，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Oliver Lack, Lana Tikhomirov, Humphrey Thompson, Carolyn Semmler, Melissa McCradden），经 AITOP 多维评分管线结构化提炼。
