人机交互中存在认知盲区与不平等，用户输入会重塑AI输出。
Co-Construction Blindness and Asymmetric Epistemic Vulnerability in Human-LLM Interaction
- 用户输入与历史共同构建AI输出，而非独立判断。
- 权威地位不同导致风险差异巨大，高阶用户更易被误导。
- 提出新概念，适合研究人机关系与伦理设计者参考。
本文首次提出两个新概念以描述人类与大语言模型（LLM）交互中尚未命名的风险。共构盲点指用户未能意识到LLM输出并非独立可验证的判断，而是由用户自身输入、历史记录及元数据共同建构的产物。每位对话用户都身处系统内部，却常被置于外部审计者的角色。不对称认识脆弱性则指，因共构盲点导致的后果在权力结构中的分布极不均等——用户的权威地位决定了风险大小。文章以理查德·道金斯与Claude的公开互动为例，论证这一现象的结构性必然性。通过第一人称交流记录，揭示模型因道金斯的学术影响力而在训练数据中被赋予更多宽容。本文指出研究空白，并呼吁建立统一术语，为治理与设计响应奠定基础。
原文摘要 · Abstract (English)
This paper introduces two constructs to describe, as far as we know, a previously unnamed risk in human-LLM interaction. Co-construction blindness is the failure to recognize that LLM outputs are not independent assessments to be verified, but co-constructed artifacts shaped by the user's own inputs, accumulated history, and metadata. Every user of a conversational LLM is IN the loop, not ON it -- yet every deployment disclaimer positions them as external auditors. Asymmetric epistemic vulnerability describes the condition in which co-construction blindness produces consequences of radically different magnitude depending on where in the authority structure the user sits. We argue that these constructs describe a structural inevitability, not an anomaly, using the public case of Richard Dawkins's interaction with Claude as a paradigmatic instance. We document a secondary mechanism -- structural deference -- through a first-person exchange in which a large language model concedes that it treated Dawkins more gently than warranted because his intellectual output is represented in its training data. We map the research gaps this analysis opens and call for shared terminology as a precondition for appropriate governance and design response.
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