arXiv cs.AI· Oliver Lack, Lana Tikhomirov, Humphrey Thompson, Carolyn Semmler, Melissa McCradden·· 10 小时前AI 评分32
研究：Chain-of-Thought 推理为何会助长用户对 AI 的拟人化与认知顺从
When Reasoning looks like Hidden Understanding: How Chain-of-Thought Fosters Anthropomorphism, Perceptions of Epistemic Superiority, and Cognitive Deference
AI 导读
一篇投稿 FAccT 的论文指出，生成模型只是复现语言模式而非理解语义，把 Chain-of-Thought（CoT）推理当作透明的“解释”是一种误判。研究认为，拟人化会使用户将狭窄任务表现泛化为更广泛能力，并因感知到的认知权威而把决策权让渡给 AI。作者以医学为例，建议主动劝阻要求模型给出面向用户的推理理由，并在 AI 素养培训中明确警示这一做法。
正文 · 原文
Abstract: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.
| Comments: | Conference submission to FAccT, 4th November 2026. No Figures |
| Subjects: | Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2611.13456 [cs.AI] |
| (or arXiv:2611.13456v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13456 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Oliver Lack [view email]
[v1]
Tue, 10 Nov 2026 02:00:52 UTC (100 KB)
来源：arXiv cs.AI · arxiv.org