Two or three years ago, declaring the use of a generative AI tool in a manuscript was an unusual courtesy. Today it is a baseline expectation, written into the author instructions of most major publishers and the recommendations of the bodies that set publishing norms. Yet the question authors most often ask is disarmingly practical: what exactly do I have to declare, and where does the declaration go? This article sets out a clear answer, drawing on the vocabulary being developed in the generative AI use and disclosure domain.
The two settled principles
Underneath the variation between publishers, two principles have hardened into near-consensus, and they are the right place to start.
The first is that a generative AI system cannot be an author. The ICMJE recommendations, and parallel statements from COPE, Nature, Science, and the major university presses, are explicit on this point: authorship entails accountability for the work, and a tool cannot be accountable. AI use is therefore disclosed as a method or a tool, never as a contributor on the author line. This connects directly to the broader account of authorship as a matter of responsibility, not merely of having touched the text.
The second is that the human authors remain fully responsible for everything the manuscript asserts, including anything an AI system produced. A fabricated citation, a misstated statistic, or a plausible-but-wrong sentence is the authors’ error regardless of which tool generated it. Disclosure does not transfer responsibility; it makes the workflow transparent so that responsibility can be located.
What counts as disclosable use
The harder question is the threshold. Not every interaction with a computational tool is a disclosable use of generative AI, and policies generally exempt the trivial. The useful distinction is whether the tool produced novel content that materially shaped the published work.
- AI-assisted writing — where a generative system drafted, restructured, summarised, or substantively edited text whose output shaped the published wording — is disclosable. A generative AI tool is, in the working definition, a system that produces novel text, code, image, or other media from a prompt, typically using a large neural network.
- AI-assisted analysis — using a model to perform or shape a data-analysis step, including exploratory analysis or hypothesis generation — is disclosable as part of the methods.
- AI-generated code that forms part of the research, and AI-generated images in a manuscript, are disclosable, the latter often under stricter rules because of the integrity risks around figures.
By contrast, most policies define an AI use exempt category for tools that do not produce novel content: a spell-checker, a grammar corrector, a reference manager, or basic translation of the author’s own words. Author-written text whose grammar was tidied by an AI checker is not, in this sense, AI-assisted writing. The line is not always crisp — substantive rewriting shades into drafting — and when in doubt the safe practice is to disclose.
Where the declaration belongs
Knowing what to declare is half the problem; the other half is placement, and here practice has converged on a small set of locations.
The dominant convention is a dedicated AI use disclosure statement in the manuscript: a short declaration that names the system, says where in the workflow it was used, and indicates the extent of that use. “Which tool, where, and how much” is the durable shape of a good statement. Many journals place this in the methods section when the use was analytical, and in a distinct acknowledgements-adjacent statement when the use was in writing.
A useful test for a disclosure statement: a reader should be able to tell, from the statement alone, which parts of the work involved a generative system and what the authors did to verify its output. A generic line that an AI tool was “used to improve readability” fails this test; it names neither the tool nor the boundary of its use.
Two adjacent practices strengthen the statement. The first is recording a model selection rationale and, where relevant, the prompt engineering that produced reliable outputs — material that belongs in supplementary methods for analytical uses, because it bears on reproducibility. The second is naming the AI tool provider at the organisational level, so that the disclosure points at an identifiable system rather than a generic category.
Why structured disclosure, not just prose
A free-text paragraph at the end of a manuscript is where most disclosures live today, and it is better than nothing. But prose disclosure has the same weakness that prose contribution statements have: it does not travel as data. A structured representation — naming the tool, the workflow stage, the extent, and the verification step as discrete, machine-readable fields — lets downstream systems index, audit, and aggregate AI use across the literature. That is the difference between a sentence a human must read and a record a system can act on, and it is the gap a controlled vocabulary is meant to close. The parallel with structured contribution metadata in CRediT is exact: a settled human-readable form, waiting on consistent machine-readable plumbing.
The role for shared vocabulary
Publishers’ AI policies differ in wording, in threshold, and in placement, which means a disclosure written for one journal does not necessarily mean the same thing when read by another system. What is missing is not more policy — the principles are settled — but a shared definitional layer: agreed terms for AI-assisted writing, AI-assisted analysis, exempt category, and the rest, so that a disclosure carries the same meaning wherever it is read. Supplying that layer, federating to ICMJE and COPE for the normative content rather than inventing it, is the convening role the CASRAI dictionary is built for. The practical guidance for authors lives at AI disclosure for authors.
What to do now
For authors: disclose any use that produced novel content shaping the work, name the tool and the workflow stage, and state that you verified the output. For editors: specify where the statement goes and ask for structured fields, not just a paragraph. For standards work: prioritise shared definitions of the disclosable categories and the exempt threshold, so disclosures mean the same thing across venues.