GoKawiilGenerative 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
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