The 14 roles of CRediT were designed in 2013-2014 with a model of contribution that did not include large language models or generative AI systems. A decade on, the taxonomy is robust and widely adopted, but the AI question is hard to ignore. This post makes the case — tentatively, and with attention to the counter-arguments — that the next CRediT revision should add a 15th role explicitly covering AI assistance. We are publishing it here to invite community pushback before any formal proposal goes to the CRediT stewardship group.
Why this question is not solved by disclosure alone
The current consensus around generative AI in scholarly authorship rests on two pillars: AI cannot be a co-author (the ICMJE 2023 position), and AI use must be disclosed in a structured declaration. CASRAI agrees with both. They do not, however, resolve the question of how AI assistance shows up in CRediT.
A worked example. Suppose a paper has four authors. Author A wrote the first draft with substantial assistance from a large language model, which she prompted, edited, fact-checked, and revised. Author B ran the formal analysis using an AI-assisted statistical-discovery tool that proposed model specifications. Author C generated several of the figures using a GenAI visualisation tool. Author D supervised. Each used AI; each used it differently; each took human responsibility for the output. How does the CRediT statement represent this?
Under current CRediT, AI use is invisible. Author A gets Writing – original draft (lead). Author B gets Formal analysis (lead). Author C gets Visualization (lead). Author D gets Supervision. The AI assistance shows up only in the publisher-mandated AI disclosure, which is a free-text field in the methods or acknowledgements. The structured contributorship record has no place for the granular fact that AI was a tool in each of those role-discharges.
The proposed 15th role
The draft scope we are testing is this:
AI assistance. The use of artificial-intelligence systems, including generative AI, machine-learning models, and automated analytical tools, in the production of the work. Includes prompt engineering, model selection, validation of AI output, and human verification of AI-generated content. Does not include use of AI as a routine tool (e.g., grammar checkers, citation-formatting tools) below a disclosure threshold defined by the publisher.
The role would carry the standard degree-of-contribution qualifier. A human author whose primary contribution was prompting and verifying an AI system would be marked Lead for AI assistance; a co-author who occasionally checked AI outputs would be Supporting. The role would not be a substitute for the existing roles — the human who used AI for the first draft still gets Writing – original draft — but it would add the structured fact that AI was involved.
The arguments for
First, structured disclosure is more useful than prose disclosure. A free-text AI declaration cannot be queried, cross-referenced, or aggregated. A CRediT-style structured role can. Integrity offices investigating a fabrication can query for papers with AI assistance roles; funders tracking AI use in grant outputs can roll up the data; bibliometric studies can analyse patterns. None of this is possible with the current free-text disclosure.
Second, granularity matters for accountability. Knowing that a paper used AI is less useful than knowing which contributor used AI for which task. The CRediT role assignment makes the accountability specific. If a fabricated reference appears in the introduction, the question of who is responsible for verifying it has a structured answer.
Third, the boundary is becoming a fiction. Modern statistical workflows include AI components (autoML, AI-assisted exploratory analysis); modern writing workflows include AI components (Copilot for prose, Claude for editing); modern visualisation workflows include AI components. The pretence that these are separable from the role they support is increasingly hard to maintain. If AI is being used to discharge a role, the role assignment should say so.
The arguments against
Three serious counter-arguments deserve engagement.
First, the scope-creep concern. CRediT has held to 14 roles deliberately. Each addition raises the cognitive load on authors filling out the statement, increases the integration burden on publishers, and risks the taxonomy becoming unusable through over-specification. The argument from Liz Allen and the original CRediT designers has been that the taxonomy gains its value from being small enough to use.
Second, the boundary problem. What counts as AI assistance? A grammar checker is plausibly AI; a citation formatter increasingly is; a search engine ranking results by relevance certainly is. If every modern research tool counts as AI, the role becomes meaningless. A workable scope requires a non-trivial threshold (the draft language above gestures at “below a disclosure threshold defined by the publisher”), and that threshold is hard to define without ending up with either everything or nothing.
Third, the disclosure-versus-contribution distinction. CRediT is a contributorship taxonomy. AI is not a contributor — that is the settled position. Adding an AI role to CRediT risks blurring this. The alternative is to keep AI in a separate disclosure form, structurally similar to a competing-interests declaration or a funding statement, rather than in the contributorship statement.
A possible middle path
The middle path is to keep CRediT at 14 roles and to define a parallel AI assistance declaration with comparable structure: a controlled vocabulary of AI-use types, a per-contributor breakdown linked to ORCID iDs, a model-and-version field, and a verification statement. This would sit alongside CRediT in publisher submission systems and JATS XML, rather than inside it.
This is closer to where the current publisher disclosure forms are heading, and it preserves the conceptual clarity that CRediT roles describe what humans did, while a separate declaration describes what AI tools were used. We are increasingly inclined to recommend this path, with the caveat that the disclosure must be structured to the same standard as CRediT — not free-text, with controlled vocabularies, deposited to Crossref, and surfaced on ORCID.
What the CRediT stewardship group should do next
Three concrete steps. First, run a structured community consultation through 2026 on whether to add AI assistance as a 15th CRediT role, with the alternative being a parallel structured declaration. The CRediT governance page outlines the consultation process. Second, in parallel, draft the data model for a parallel AI assistance declaration so that the comparison is concrete and not abstract. Third, coordinate with NISO on whether either option requires a revision to Z39.104.
The decision is not urgent in the sense that the integrity system is failing today; the existing disclosure forms work, badly. It is urgent in the sense that every year of delay produces another year of unstructured AI-use data that cannot be aggregated or analysed, which makes the eventual transition harder.