当 AI 协助科研写作时，谁应负责？

Email Bluesky Facebook LinkedIn Reddit Whatsapp X Save article View saved research Illustration: Marie Wolf Generative artificial intelligence (genAI) tools are increasingly being used in science to search and summarize literature, produce ideas, draft responses to reviewers and improve the style of written text. This raises a question: what counts as human authorship when some of a researcher’s work is mediated by AI? And how much of this intellectual ‘work’ should be attributed to an AI model or its developers? Authorship in science is an earned status: it recognizes a scholar’s contribution to a paper and signals to the research community that they are a competent and responsible scientist. What is at stake in potentially recognizing AI as an author is not simply who gets credit for a text, but how scholarly writing is deemed accountable and trustworthy. AI, peer review and the human activity of science This problem pre-dates genAI. Early modern authorship was not a natural or fixed status but a practical achievement, established through conventions. Isaac Newton’s authority, for instance, rested not only on scientific discovery, but also on his ability to navigate the institutions such as the Royal Society and publication practices through which scholarly credit was assigned. New technologies have repeatedly reshaped these arrangements. Ultimately, current decisions regarding AI’s role in academic publishing have a bearing on the future of scholarship itself1. If AI is increasingly used to screen submissions, assist with peer review, create grant proposals, evaluate research outputs and shape publishing workflows, then decisions about its use should not be driven solely by convenience or cost saving. More explicit public debate is needed. Here, I examine some of the challenges for scholarship surfaced by the advent of AI and outline steps forwards. AI-mediated authorship Academic writing has long been organized through differentiated, hierarchical labour. In a research team, junior members might search the literature and collect data. More experienced scientists can develop the conceptual framing of a paper and draft its sections, and senior researchers often supervise, revise and approve the final version of the work. When authors use genAI tools to do part of their work, it’s not equivalent to bringing in a human contributor. A graduate student or research assistant can explain their contribution, respond to criticism, learn from correction and be held accountable for the integrity and accuracy of their work. GenAI is different: it cannot justify or take responsibility for what it produces. The uncritical adoption of AI in science is alarming — we urgently need guard rails Use of genAI involves a set of technologically mediated practices: writing prompts, generating outputs, summarizing information, classifying content and checking results. These actions fall outside current authorship categories — and thus authorship cannot reveal who did what, how GenAI was involved or how credit and responsibility should be assigned. Without clear rules for AI-mediated work, authorship becomes harder to account for. Existing authorship-role taxonomies, such as CRediT — the Contributor Roles Taxonomy, now used by several publishers, which specifies the roles researchers had in producing a paper — are both necessary and insufficient. They remain valuable because they break down scholarly labour into specific roles, going beyond the misconception that author order alone captures contributions. But CRediT was built for human contributors. Human–genAI writing upends it; current reporting structures barely register AI mediation. Beyond the writing team, the politics of authorship extends through the editorial and review infrastructures that manuscripts must go through to be considered scholarship. AI has started playing a part in this arena, too. Reviewer selection, editorial interpretation of reports and the language of decisions — all of which can be influenced by AI use — shape the fate of the text and the authority of its eventual authors. AI scientists are changing research — institutions, funders and publishers must respond The politics of authorship is particularly evident in the acknowledgements section. These statements are not just courtesies. They are part of a graded economy of attribution through which scholarly labour is valued or left invisible. Some work becomes authorship, some gets downgraded to ‘helpful comments’ or ‘assistance’ and some disappears. In this section, research assistants, students, reviewers and colleagues encountered at workshops, conferences or dinner tables might be mentioned, named and thanked — or left out. Such omissions extend to the largely invisible human labour that makes genAI systems work: data labelling, model evaluation, engineering work, platform maintenance and the production of the texts on which the models are trained. This is why the current debate should be about more than solving the problem of how to fit genAI into an existing graded economy of attribution. It is an opportunity to question that economy itself. By necessitating new categories of authorship practices in existing frameworks, genAI can create an opening to destabilize some hierarchies in academic work, rather than merely adding a new tool to the workflow: if AI-assisted literature synthesis is recognized through authorship, why would the same work, when performed by a human research assistant, not earn them authorship as well? Enjoying our latest content? Log in or create an account to continue Access the most recent journalism from Nature's award-winning team Explore the latest features & opinion covering groundbreaking research Access through your institution or Sign in or create an account Continue with Google Continue with ORCiD Nature 657, 34-36 (2026) doi: https://doi.org/10.1038/d41586-026-02686-z GenAI (OpenAI ChatGPT 5.4) was used as a dialogical writing and analytical aid during the development of the piece, including for idea generation, structural iteration, prose generation, summarization, condensation and stylistic revision. It also assisted in synthesizing relationships between the cited papers and the author’s uploaded manuscripts. GenAI (Anthropic Claude 4.2) was used to review the article. Review comments have been addressed during dialogical revision completed with ChatGPT. References Rees, G. & Wilsdon, J. Nature 652, 1119–1121 (2026). Article PubMed Google Scholar Earp, B. D., Guernon, A.-S. & Porsdam Mann, S. Nature Rev. Bioengin. 4, 479–480 (2026). Article Google Scholar Download references Reprints and permissions Competing Interests The author declares no competing interests. Related Articles AI, peer review and the human activity of science A ten-year drive to credit authors for their work — and why there’s still more to do The uncritical adoption of AI in science is alarming — we urgently need guard rails AI scientists are changing research — institutions, funders and publishers must respond More than half of researchers now use AI for peer review — often against guidance Subjects Authorship Ethics Machine learning Scientific community Latest on: Authorship Ethics Machine learning When AI does science, who is accountable for mistakes? Correspondence 01 SEP 26 ‘A waste of time for all of us’: caught in the crossfire of the peer-review wars Career Feature 03 AUG 26 Could small-team science be dying out? 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