AI-Generated Papers and Journal Integrity

Two quite different things are discussed under one heading, and almost all the confusion comes from that. One is a researcher using a model to draft, edit or translate work they did. The other is fabricated content submitted to inflate a publication record. The first is a disclosure question. The second is fraud, and it is not new. Two problems wearing one name A non-native English speaker using a model to make their methods section readable has done nothing wrong and has improved the literature. A paper mill generating plausible manuscripts at volume has committed fraud, and would have done so with or without a language model — mills existed, using image manipulation, template text and fabricated data, long before this technology arrived. Keeping them apart matters because they call for opposite responses. The first needs a disclosure norm and nothing else. The second needs content verification, and content verification does not care what tool produced the content. Any policy built around detecting machine text will punish the first group and miss most of the second, because fabricated research that has been lightly rewritten is indistinguishable from careful assisted writing. It is also worth being clear about where the demand comes from, because it explains why no technical measure will resolve this. Paper mills exist because publication counts are used as a proxy for research contribution in hiring, promotion and institutional ranking, in systems large enough that buying an authorship is a rational purchase for some buyers. Generative tools lowered the cost of supplying that demand; they did not create it. A detector, even a perfect one, sits downstream of an incentive that would simply route around it — which is why the interventions with the best track record are the ones that attack verifiability, such as requiring data and code, rather than the ones that attack production. What the artefacts look like Leftover interface text. Phrases that belong to a chat in