arXiv cs.AI· Kalin Stoyanov, Nina Ilieva·· 12 小时前AI 评分21
伦理视角下的 LLM 辅助科研：负责任委托、验证与认知价值框架
Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value
AI 导读
一篇 arXiv 论文提出 LLM 辅助科研的规范框架，将科学推理视为人机分布式过程，区分内容来源 O(g)、人工验证完成度 V(g)、责任分配 R(g)、可问责的人类所有权 M(g) 与认知结果 E(g)。核心主张是伦理边界主要由充分验证与可问责的人类所有权决定，而非机器参与程度本身，并据此提出“认知审计”概念，用于结构化记录委托、验证、来源与责任。
正文 · 原文
Abstract: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.
| Comments: | This 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 |
| Subjects: | Artificial Intelligence (cs.AI) |
| MSC classes: | 68T01 |
| ACM classes: | I.2.0; K.4.1; K.7.4 |
| Cite as: | arXiv:2608.23644 [cs.AI] |
| (or arXiv:2608.23644v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.23644 arXiv-issued DOI via DataCite |
Submission history
From: Kalin Stoyanov [view email]
[v1]
Mon, 24 Aug 2026 07:13:34 UTC (27 KB)
[v2]
Tue, 20 Oct 2026 11:20:17 UTC (14 KB)
来源：arXiv cs.AI · arxiv.org