AI生成内容隐性抄袭他人思想，引发学术溯源危机。
The Provenance Problem: LLMs and the Breakdown of Citation Norms
- 提出'溯源问题'概念，揭示AI引用无意识泄露他人成果
- 指出非故意抄袭也构成学术信用伤害，现有规范无法应对
- 适合关注AI伦理、学术诚信的研究者与期刊编辑阅读
生成式AI在科研写作中的普及引发对署名与智力贡献归属的紧迫问题。当研究人员使用ChatGPT撰写稿件时，文本可能无意中重复从未接触过的文献观点。若AI系统复制了某篇冷门1975年论文的洞见却未标注来源，这是否构成剽窃？我们认为此类情况体现了‘溯源问题’：学术信用链条的系统性断裂。不同于传统剽窃，此现象并无欺骗意图（研究者可能主动披露使用AI且出于善意），但仍无偿获益于他人的未被承认智力贡献。这种动态形成一种新型归因伤害，当前伦理与职业框架均未能应对。随着生成式AI跨学科渗透，重要思想在无认可情况下传播，威胁科学声誉体系与认识论正义。本文分析AI如何挑战既有作者权规范，提出理解溯源问题的概念工具，并建议维护学术交流完整性与公平性的策略。
原文摘要 · Abstract (English)
The increasing use of generative AI in scientific writing raises urgent questions about attribution and intellectual credit. When a researcher employs ChatGPT to draft a manuscript, the resulting text may echo ideas from sources the author has never encountered. If an AI system reproduces insights from, for example, an obscure 1975 paper without citation, does this constitute plagiarism? We argue that such cases exemplify the 'provenance problem': a systematic breakdown in the chain of scholarly credit. Unlike conventional plagiarism, this phenomenon does not involve intent to deceive (researchers may disclose AI use and act in good faith) yet still benefit from the uncredited intellectual contributions of others. This dynamic creates a novel category of attributional harm that current ethical and professional frameworks fail to address. As generative AI becomes embedded across disciplines, the risk that significant ideas will circulate without recognition threatens both the reputational economy of science and the demands of epistemic justice. This Perspective analyzes how AI challenges established norms of authorship, introduces conceptual tools for understanding the provenance problem, and proposes strategies to preserve integrity and fairness in scholarly communication.
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