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On the troubling rise of generative AI suspicion in academic publishing

Nature Machine Intelligence (2026)Cite this article

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Scientific writing has changed in unprecedented ways since the release of generative artificial intelligence (GenAI) and large language models (LLMs)1, which has raised legitimate concerns about epistemic integrity while also fostering an adversarial climate in which suspicion begins to substitute for evidence of wrongdoing. These concerns are warranted but are often overextended. Detection of the use of AI is increasingly framed as a quasi-forensic exercise, in which unusual phrasing or syntax is treated as incriminating evidence, amounting, by some accounts, to an ‘AI witch hunt’2. Initially, suspicion centered on a small set of markers, such as ‘delve’, ‘crucial’ or ‘pivotal’. As scrutiny intensified, this list expanded, with ever-more-conventional expressions treated as indicators of LLM involvement1. The result is an elastic standard that allows almost any stylistic choice to be construed as evidence of machine authorship.

The technical limits of such judgments are well established. Stylometric detectors operate probabilistically and cannot reliably distinguish a human who writes formally from a machine trained on similar corpora3,4,5. As academic writing converges toward shared disciplinary registers, human authors increasingly seem to be ‘LLM like’. The resulting prevalence of false positive results4,5 calls into question the epistemic basis on which suspicion is exercised.

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Acknowledgements

R.C. declares using ChatGPT-5.2 for the sole purpose of language editing assistance. All outputs were manually validated and refined, with full responsibility for the content retained by the author.

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  1. Business Information Systems, University of Sydney, Sydney, New South Wales, Australia

    Raffaele Ciriello

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  1. Raffaele Ciriello
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Correspondence to Raffaele Ciriello.

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Ciriello, R. On the troubling rise of generative AI suspicion in academic publishing. Nat Mach Intell (2026). https://doi.org/10.1038/s42256-026-01178-z

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