发表机构
University of Chemical Technology and Metallurgy（化工技术与冶金大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建了伦理LLM辅助研究的规范性框架，明确其伦理边界由充分验证与可问责人类所有权决定，提出认知审计概念以实现AI辅助推理的透明可审查性。
AI 中文摘要
大语言模型（LLM）正成为科研领域的常规工具，协助文献综合、假说开发、编码及形式推理。其应用引发了核心认知问题：当部分科学推理被委托给人工系统时，为使产生的知识主张保留认知合法性与可问责的作者身份，人类必须控制哪些条件？本文构建了用于分析此类委托的规范性与概念性框架。科学推理被视为分布式过程，其中贡献的来源可能在人类与机器之间变化，而其被纳入科学记录的责任仍归人类。该框架区分了内容起源$O(g)$、人类验证完成$V(g)$、责任分配$R(g)$、可问责人类所有权$M(g)$及认知结果$E(g)$。这些构念将主张的来源与其检查过程、检查的认知结果及相关人类责任分离开来。核心主张是，LLM辅助研究的伦理边界主要由充分的验证与可问责的人类所有权决定，而非机器参与的程度。在此基础上，本文提出了“认知审计”的概念：一种包含委托、验证、来源与责任的结构化记录，旨在使AI辅助推理透明化并可被审查。该框架为区分负责任的认知委托与科研中认知责任的转移或忽视提供了正式术语。
英文摘要
Large language models (LLMs) are becoming routine instruments of scientific research, assisting with literature synthesis, hypothesis development, coding, and formal reasoning. Their use raises a central epistemic question: when parts of scientific reasoning are delegated to an artificial system, what conditions must remain under human control for the resulting knowledge claims to retain epistemic legitimacy and accountable authorship? This paper develops a normative and conceptual framework for analyzing such delegation. Scientific reasoning is treated as a distributed process in which the origin of a contribution may vary between human and machine, while responsibility for its acceptance into the scientific record remains human. The framework distinguishes content origin $O(g)$, completion of human verification $V(g)$, responsibility assignment $R(g)$, accountable human ownership $M(g)$, and epistemic outcome $E(g)$. These constructs separate the provenance of a claim from the process by which it is checked, the epistemic outcome of that checking, and the human responsibility attached to its disposition. The central proposition is that the ethical boundary of LLM-assisted research is determined primarily by adequate verification and accountable human ownership rather than by the degree of machine involvement itself. On this basis, the paper develops the notion of an \emph{epistemic audit}: a structured record of delegation, verification, provenance, and responsibility intended to make AI-assisted reasoning transparent and reviewable. The resulting framework provides a formal vocabulary for distinguishing responsible cognitive delegation from the transfer or neglect of epistemic responsibility in scientific research.
CommentsThis is a substantially revised version of submit/7664457. I have extensively revised the manuscript to clarify its scholarly contribution, strengthen the formal framework and literature grounding, and remove or reformulate claims that were not sufficiently supported
