Blog

The ICMJE 2023 position is settled: artificial-intelligence systems cannot be authors. The follow-on question that journals and authors continue to negotiate is how to represent, in a contributorship statement, the human work that goes into producing AI-assisted content. When a co-author prompts an LLM to draft a section, verifies the output, edits it, and stands behind it, which CRediT role describes their contribution? This post proposes a working line.

The shape of the question

Three scenarios make the question concrete.

Scenario one: an author uses an LLM to polish prose in a draft they wrote. The intellectual content is theirs; the language is partly the model’s. The CRediT role is straightforwardly Writing – original draft for the author.

Scenario two: an author uses an LLM to draft a first version of a section, which they then heavily revise. The first draft is the model’s; the final draft is the author’s, but the model substantively shaped what the final draft says. The CRediT role is still Writing – original draft for the author, but the contribution is meaningfully different from scenario one.

Scenario three: an author uses an LLM to propose a study design, which they then refine. The intellectual content of the methodology was partly the model’s. The CRediT role is Methodology for the author, but again the contribution is meaningfully different from the unaided version.

In all three, the human author is the role-holder; the model is not a co-author. What is different across the scenarios is the magnitude and the character of the human contribution. CRediT, as currently constituted, does not distinguish these.

The working line

Our proposed line is the verification-and-responsibility threshold. A human contributor who has substantively verified the AI-generated content, taken responsibility for it, and is prepared to defend it in correspondence or post-publication discussion is properly credited with the relevant CRediT role. The role describes what they contributed to the paper, which includes verification work even if the first-draft work was the model’s.

The line shifts where the human contribution is insubstantial — a contributor who pasted a prompt, accepted the output without verification, and added their name to the paper has not discharged the role and should not be credited. This is the same line that has always applied to non-AI cases (a co-author who did not contribute should not be credited; gift authorship is a well-recognised failure mode).

The line is therefore not about AI use per se; it is about whether the human contribution clears the substantive-contribution threshold. AI use does not displace the threshold; it changes what discharging the role looks like in practice.

Disclosure runs parallel

The disclosure of AI use is a separate question, addressed via publisher-mandated AI disclosure declarations. The disclosure says what tools were used and for what; the CRediT statement says who contributed what to the paper. The two run parallel and are both required by most major publishers in 2026. The CASRAI AI disclosure for authors guide walks through the publisher-by-publisher requirements.

Implications for specific roles

Writing – original draft

The most common case. A human author whose draft was AI-assisted is properly credited with Writing – original draft if they verified the content, took responsibility for it, and produced the version that is the paper. The disclosure declaration says the AI was used; the CRediT statement names the human as the writer-of-record.

Methodology and Formal analysis

More delicate. If an AI-assisted statistical-discovery tool proposed a method or an analytic approach, the human contributor’s role is partly verification (was the proposal sound?) and partly extension (refining the proposal into the actual method). The CRediT role is still Methodology and/or Formal analysis for the human, but the verification dimension is foregrounded. If the human did not verify — accepted the AI proposal without independent assessment — the contribution is weaker and may not clear the role threshold.

Investigation

A subtle case. AI-assisted data extraction (e.g., from imaging, from medical records, from text corpora) involves a human contribution that runs from setup through verification to interpretation. Investigation includes the data-gathering activity; an AI-assisted version still has a human Investigation lead, who is responsible for the setup, the verification of extracted data, and the handling of errors.

Validation

Perhaps the most directly affected. Where AI tools are used for cross-checking, sensitivity analyses, or reproduction of results, the human Validation contributor is responsible for setting up the validation, interpreting its results, and acting on discrepancies. The AI does the mechanics; the human does the judgement.

Visualization

AI-assisted figure generation is increasingly common. The human Visualization contributor is responsible for the figure-design decisions, for verifying that the AI-generated figure accurately represents the data, and for the final version that appears in the paper. Where the AI generated an image that the human did not substantively verify, the threshold may not be cleared.

The role-as-recognition trap

A failure mode to flag explicitly. The temptation, when AI did most of the actual production work, is to inflate the human contributor’s role assignment to compensate. “The AI wrote the draft, but I prompted it, so I should still be Lead on Writing – original draft.” This is a misreading. The CRediT role is a description of contribution; if the human contribution was “prompted and accepted”, that is a smaller contribution than “drafted, verified, revised, took responsibility.” Calling both “Lead” obscures the difference.

The remedy is the degree-of-contribution qualifier. A human contributor whose AI-assisted contribution was substantial may be Lead; one whose contribution was lighter may be Supporting. The qualifier discipline forces an honest assessment of magnitude.

Where this leaves the AI-assistance-role question

We have argued elsewhere that a 15th CRediT role explicitly for AI assistance is worth considering. The argument from this post is partly orthogonal: the existing 14 roles can accommodate AI-assisted work if the verification-and-responsibility threshold is honoured and the qualifier is used honestly. The case for a 15th role rests on whether the structured disclosure-of-AI-use is better placed inside the contributorship statement or outside it. Reasonable people disagree; we lean toward keeping AI disclosure parallel to CRediT rather than inside it, with attention to the verification-and-responsibility line.

Practical recommendations

Three for authors. First, treat AI assistance as a tool, not a substitute. Verify, edit, and take responsibility for what appears in the paper. Second, assign CRediT roles based on what you contributed including verification, not based on what the AI produced. Third, disclose AI use in the publisher-mandated declaration; the disclosure runs parallel to CRediT, not inside it.

Three for editors. First, treat the verification-and-responsibility threshold as the operating standard for AI-assisted contributorship. Second, require both the CRediT statement and the AI-use disclosure at submission. Third, where a contributorship statement looks like it may reflect AI-assistance role inflation, ask the standard editorial question: what did this contributor actually do?

Three for the broader system. First, harmonise AI-disclosure formats across publishers (work the NISO and COPE community has begun). Second, maintain the contributorship-versus-disclosure separation; do not collapse them. Third, evaluate the case for a 15th CRediT role on its merits, including the costs of taxonomic expansion.

Related dictionary entries

• CRediT – Contributor Roles Taxonomy
• AI co-authorship rejection (ICMJE 2023)
• AI-assisted writing
• Degree of contribution qualifier
• Hallucination (LLM)
• Prompt engineering
• Large language model (LLM)
• AI output verification