Dictionary domainTrack A
Generative AI use and disclosure
Vocabulary for human–AI collaboration on research outputs and the disclosure required.
For implementers
Operational deployment checklist for Generative AI use and disclosure: prerequisites, five deploy steps, integration notes for Pure, Symplectic Elements, Worktribe, DSpace, and more, plus the pitfalls that recur in the field.
Terms in this domain
45 terms
NSF AI Dear Colleague Letter (DCL)
An NSF AI Dear Colleague Letter (DCL) is any of several topic-specific Dear Colleague Letters the National Science Foundation has issued that have artificial intelligence as their explicit subject -- typically flagging a funding priority and directing proposers toward an existing mechanism (RAPID, Planning Grant, EAGER, or a standing program) rather than creating an independent competition. To count as one, a document must (a) be formally issued as a DCL under NSF's Proposal & Award Policies & Procedures Guide (PAPPG) Chapter I definition, carrying an NSF publication number in the nsf.gov DCL series, and (b) name AI/generative AI as the letter's subject. This is a narrower category than 'NSF AI policy' generally, and it explicitly excludes NSF's December 2023 guidance on generative-AI use in the merit review process, which was issued as a Notice to the Research Community, not a DCL -- a distinction searchers frequently get wrong and this page exists to clarify.
ICMJE Generative AI Policy
The International Committee of Medical Journal Editors (ICMJE) sets out its rules on generative AI in Section II.A.4 of its Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals, added in the May 2023 update. The policy rests on three linked rules: (1) chatbots and other AI tools cannot be listed as authors because they cannot take responsibility for the accuracy, integrity, and originality of the work and cannot approve a final version for submission, so they fail ICMJE's own authorship criteria by definition; (2) at submission, authors must disclose whether AI-assisted technologies were used, with the disclosure location depending on how the tool was used — writing, editing, or proofreading assistance is described in the Acknowledgments section, while use of AI to collect or analyze data or to generate figures is reported in the Methods section; and (3) human authors remain fully responsible for any submitted material produced with AI assistance, including reviewing and editing AI output carefully, because such tools can generate authoritative-sounding text that is incorrect, incomplete, or biased.
DOE AI Policy
"DOE AI policy" most precisely refers to the U.S. Department of Energy's Generative Artificial Intelligence Policy (DOE P 2031, "Use of Generative Artificial Intelligence," issued December 29, 2025) -- a departmental directive governing how DOE's own federal workforce and National Laboratory staff may adopt, deploy, and use generative AI tools in their work. It sits inside DOE's broader 200-series management directives and was written to align with the October 2023 White House Executive Order on AI and the March 2024 OMB Memorandum M-24-10 on federal agency AI governance. It is an internal governance instrument, not an applicant-facing submission requirement: it does not create a DOE-wide rule that external grant applicants, proposers, or awardees must disclose generative AI use when submitting a funding opportunity announcement (FOA) response. Where DOE's policy does touch research practice directly is inside the agency: when generative AI contributes to an idea, approach, or invention developed at DOE or a National Laboratory, DOE staff are expected to identify that specific contribution and cite the GenAI tool as part of the research methodology, consistent with the department's broader research-integrity and record-keeping expectations. Research administrators should not assume this term is interchangeable with the applicant-facing generative AI disclosure notices published by other funders -- see NSF AI Policy and Nature Portfolio AI Policy for that different category of policy -- and should always check the specific DOE FOA's own instructions and applicable acquisition regulations for any submission-level AI-disclosure requirement, since DOE has not, as of this writing, published a separate proposer-facing AI-disclosure notice comparable to NSF's.
IEEE Generative AI Policy
IEEE has no single unified 'AI policy' document; instead it implements two distinct sets of generative-AI rules, disseminated consistently across its journals, transactions, and conferences via the IEEE Author Center and individual IEEE technical societies. The author-side track requires disclosure of generative-AI use in preparing a manuscript, forbids listing an AI system as a co-author, and holds human authors fully accountable for the accuracy and originality of every word, figure, and citation regardless of how it was drafted. The reviewer-side track is a separate and stricter confidentiality rule: reviewers may not upload any part of a manuscript under review to a public generative-AI platform, and may not use such a platform to draft part or all of a review, because doing so risks exposing confidential, unpublished material to a system that can retain or learn from submitted input.
Nature Portfolio AI Policy
Nature Portfolio's AI policy is the set of editorial rules, published on nature.com and applied across Nature, its sister research and Nature Reviews journals, Scientific Reports, and other Nature Portfolio titles, governing generative AI use in submitted manuscripts. A submission is treated as compliant only if: (1) no AI tool (large language model, chatbot, or image generator) is listed as an author or co-author, since authorship requires accountability that a tool cannot hold; (2) any substantive use of an LLM or other generative AI tool in preparing the manuscript is disclosed in the Methods section (or an equivalent alternative section for article types without one) — minor AI-assisted copyediting of already-written text does not require disclosure; and (3) no AI-generated or AI-substantially-modified image, video, or figure is used, except in the narrow case of a piece specifically about AI itself, reviewed case-by-case by the editor, or artwork sourced through an agency with a contractual chain that makes its provenance legally clear.
NSF AI Policy
NSF AI policy is not a single rule but a set of related National Science Foundation positions on artificial intelligence that a proposer or awardee needs to track separately: (1) guidance, currently framed as encouraged rather than mandatory, on disclosing generative-AI use in preparing a proposal; (2) an explicit statement that fabrication, falsification, or plagiarism committed with the assistance of AI-based tools counts as NSF research misconduct under the Proposal & Award Policies & Procedures Guide (PAPPG); and (3) NSF's own AI research-infrastructure investments, chiefly the National AI Research Resource (NAIRR) and the NSF AI Institutes program, which are funding/access policy rather than proposal-conduct policy. Treat these as three distinct tracks that happen to share the same agency and the same underlying 2023-2025 policy push, not one unified 'AI rule.'
OECD AI Principles
The OECD AI Principles are the first intergovernmental standard on artificial intelligence, adopted by OECD member and partner governments in May 2019 and updated in May 2024 to address general-purpose and generative AI. They consist of five values-based principles for trustworthy AI (inclusive growth/sustainable development/well-being; human rights, democratic values and fairness including privacy; transparency and explainability; robustness, security and safety; and accountability) plus five recommendations for policymakers (invest in AI research and development; foster an inclusive AI-enabling ecosystem; shape an enabling, interoperable governance and policy environment; build human capacity and prepare for labour-market transition; and pursue international co-operation for trustworthy AI). As of the 2024 update, 47 countries and jurisdictions adhere to the Principles, including all OECD members, the European Union, and several non-member states. A research institution or research-performing organization can treat the Principles as an operational baseline for AI governance when: (1) it can point to a documented AI risk-management or oversight process addressing all five values-based principles (not just one, e.g. privacy) as applied to a specific AI use case in the research lifecycle; (2) that process is proportionate to the AI system's stage and context of use rather than a single one-time sign-off; and (3) it is paired with actual transparency to affected parties (participants, authors, reviewers) about where and how AI was used. Simply having an internal 'AI policy' document does not, on its own, satisfy the Principles — the OECD frames them as principles for actors across the AI system lifecycle, not a checklist to file away.
ACM Policy on Authorship
The ACM Policy on Authorship is the Association for Computing Machinery's publisher-wide rule set, applied across its journals, transactions, magazines, and 70+ conference proceedings, defining who may be listed as an author on an ACM-published work, prohibiting generative AI tools from being listed as authors, and requiring authors to disclose any use of generative AI tools and technologies in producing the work — with a narrow exemption for basic word-processing aids such as spelling and grammar checkers.
Fake Citation
A fake citation (also called a fabricated citation or, when the source is a paper never written at all, a phantom reference) is a reference that does not correspond to a real, findable publication -- an invented author, title, journal, DOI, or page range, or some combination of these, presented as if it points to an actual source. It also covers the narrower case of a citation to a real work that is misrepresented: the cited paper exists, but it does not say, show, or support what the citing text claims it does. A citation is operationally 'fake' if a reader who tries to locate and check it cannot verify that the source exists as described, or finds that the source exists but does not support the claim attached to it -- as opposed to an honest error (a typo in a volume number, a wrong year) that still resolves to the intended real work.
Consensus (AI Academic Search Engine)
Consensus is a named AI-powered academic search engine (built by the company Consensus, at consensus.app) that retrieves peer-reviewed papers relevant to a natural-language research question and generates a synthesis of what the retrieved literature says, rather than returning a plain ranked list of results. For questions phrased as a yes/no/maybe claim, it additionally displays a 'Consensus Meter' -- a visual indicator of how the retrieved papers' findings line up (agree, disagree, or mixed) on that specific claim, generated from the paper set Consensus itself retrieved and summarized. It is a specific product in the 'literature summarization and evidence-synthesis' category, distinct from general-purpose AI chatbots (which do not search a dedicated indexed academic corpus or cite retrieved papers by default) and from citation-graph or general-purpose scholarly search tools such as Semantic Scholar (which surface and rank papers but do not generate a claim-level agreement synthesis across them).
AI Research Tool
An AI research tool is software that applies machine learning — typically a large language model (LLM), an embedding-based semantic search index, or both — to a specific stage of the research workflow: finding and screening literature, extracting or summarizing data from papers, mapping citation relationships, drafting or revising manuscript text, or analyzing research data. "AI research tool" is a category label, not the name of any single product: it covers named tools with genuinely different scopes (a citation-mapping tool like Connected Papers does not do what a writing-assistance tool like Paperpal does), and a page or citation that treats it as one interchangeable thing is usually mis-scoped. What makes a tool an instance of this category, rather than a general-purpose AI assistant that happens to get used for research, is that it is built or marketed specifically around a research task — an academic search index, a citation graph, a manuscript-formatting model trained on published literature — rather than being a general chat interface pointed at an arbitrary prompt.
Elicit (AI Research Assistant)
Elicit is a named AI research-assistant platform (built by the public benefit corporation Elicit, spun out of the nonprofit lab Ought in 2023) that searches an indexed academic-literature corpus and performs structured evidence-extraction tasks -- summarizing papers, extracting and tabulating data points across many papers with sentence-level source citations, and supporting systematic-review-style screening and data-extraction workflows aligned to PRISMA 2020. It is a specific product, not a generic label for "AI that helps with research" -- distinct from citation-graph discovery tools (Connected Papers, ResearchRabbit) and general-purpose AI writing assistants -- and its extraction/screening output requires independent verification against the source papers rather than being treated as ground truth.
Model versioning
The practice of identifying a specific revision of an AI model by name, version number, release date, or content hash, sufficient to uniquely distinguish it from earlier or later revisions that may behave differently on the same input.
Inference
The process of generating outputs from a trained AI model in response to inputs at runtime, distinct from training (which updates model parameters); for LLMs, inference is the production of completions from prompts.
Fine-tuning
The process of further training a pre-trained foundation model on a smaller, task-specific or domain-specific dataset, updating some or all parameters, to specialise its behaviour while retaining general capability.
Retrieval-augmented generation (RAG)
An AI architecture in which an LLM is augmented at inference time with documents retrieved from an external corpus (often via vector similarity search), so that the model's outputs are grounded in retrieved evidence rather than relying solely on parametric knowledge.
AI in literature search
The use of AI-powered tools (Elicit, Consensus, Scite, Undermind, Semantic Scholar's AI features) to discover, screen, summarise, or synthesise scholarly literature, distinguished from traditional keyword search by the use of embeddings, LLM summarisation, or generative answering over a literature corpus.
AI in qualitative coding
The use of LLMs or other AI tools to assign codes, themes, or categories to qualitative data (interview transcripts, open-ended survey responses, field notes), either as the sole coder, a second coder for reliability, or a first-pass triage to be human-reviewed.
AI image generation
The use of a generative model (diffusion models, GANs, autoregressive image models) to produce images from text prompts, image prompts, or other conditioning inputs, distinct from AI-assisted image editing of an existing real image.
AI summarisation
The use of an AI system to produce a shorter version of a text that preserves its key information, including abstracts, lay summaries, executive summaries, and literature digests, whether extractive (sentence selection) or abstractive (paraphrasing).
AI translation
The use of an AI system (rule-based, statistical, or neural) to convert text from one natural language to another, including specifically the use of LLMs and dedicated neural machine translation systems for translating scholarly works or portions thereof.
AI in editorial decisions
The use of AI tools by journal editors to triage, screen, or make decisions on submitted manuscripts, including desk-rejection screening, reviewer matching, plagiarism flagging, or assessment of fit — distinguished by whether the AI advises a human editor or substitutes for one.
AI in peer review
The use of generative AI tools by peer reviewers to assist in evaluating manuscripts — including summarisation, language editing of the review, or generation of review text — which is restricted or prohibited by many publishers due to confidentiality and originality concerns.
Generative-AI disclosure statement
A dedicated section in a manuscript — typically headed 'Use of Generative AI' or similar — that consolidates all disclosures of AI tool use across the work, including tools used, versions, sections affected, and the human authors' verification process.
AI co-authorship rejection (ICMJE 2023)
The 2023 update to ICMJE's Recommendations stating explicitly that chatbots and generative AI systems cannot be listed as authors because they cannot satisfy any of the four ICMJE authorship criteria, in particular the requirement to be accountable for the work and to approve the version to be published.
Author responsibility (for AI use)
The principle that human authors retain full responsibility for the accuracy, integrity, originality, ethical sourcing, and lack of plagiarism of all content in a scholarly work, regardless of which portions were drafted, suggested, or generated by an AI tool.
Acknowledgement (vs authorship for AI)
The convention, codified by ICMJE and COPE (2023), that AI tool use must be disclosed in the methods or acknowledgements section of a scholarly work rather than via the author byline or CRediT contributor list, because AI cannot satisfy authorship's accountability requirements.
Training data provenance
Documentation of the sources, collection methods, licensing, consent basis, time range, and processing steps applied to the data used to train an AI model, sufficient to assess fitness-for-purpose, legal compliance, and potential bias.
Data leakage (training)
The contamination of an AI model's training corpus with data that should have remained held-out — including evaluation benchmarks, test sets, or proprietary content — such that the model's apparent performance overstates its true generalisation ability or it can reproduce content it should not have seen.
AI fairness
A property of an AI system whereby its outputs satisfy a defined criterion of equitable treatment across specified groups — common criteria include demographic parity, equalised odds, equal opportunity, and calibration parity — recognising that these criteria are often mutually incompatible.
AI bias
Systematic skew in an AI system's outputs that produces unjustified differential treatment, accuracy, or representation across groups, tasks, or contexts, arising from training-data composition, model architecture, objective function, or deployment context.
Detection tool (AI-generated)
A software system that estimates the probability that a given piece of content (text, image) was produced by a generative AI system, typically by analysing statistical features of the output without access to provenance metadata.
AI provenance
The chain of evidence — metadata, cryptographic signatures, watermarks, or attestations — that documents whether a piece of content was produced or modified by an AI system, by which system, when, and under what prompt or input.
Watermarking (AI output)
The embedding of a statistical, cryptographic, or visible signal into AI-generated content at the time of generation, allowing later identification of that content as machine-produced — distinct from post-hoc detection which infers AI origin from features of the output alone.
Synthetic image
An image produced by a generative model (e.g., diffusion model, GAN) rather than captured from a physical scene or instrument, including images used as illustrations, in figures, or as training data.
Synthetic data
Data generated by a model or algorithm rather than collected from real-world observations or experiments, designed to mimic the statistical structure of real data for purposes such as augmentation, privacy-preservation, or model training.
Hallucination
An output from a generative AI system that is presented confidently and fluently but is factually incorrect, fabricated, or unsupported by the input data or any verifiable source — including invented citations, non-existent authors, false statistics, and incorrect quotations.
System prompt
A prompt provided to an LLM at the start of a session — typically not visible to the end user — that sets persona, constraints, output format, safety rules, and tool-use permissions for all subsequent user interactions in that session.
Prompt engineering
The practice of designing, refining, and structuring input text given to a generative AI system in order to elicit specific desired outputs, including techniques such as role assignment, few-shot examples, chain-of-thought scaffolding, and output-format specification.
Generative AI
Artificial intelligence systems whose primary output is novel content (text, images, audio, video, code, or structured data) produced by sampling from a learned distribution, as distinct from discriminative AI systems whose output is a classification, score, or decision over existing inputs.
Large language model (LLM)
A neural-network model trained on large text corpora using self-supervised next-token prediction (or analogous objective), with parameter counts typically in the billions, capable of generating coherent text and performing a broad range of natural-language tasks without task-specific training.
AI tool disclosure
A statement within a scholarly work that identifies which generative AI tools were used, the version, the scope of use (e.g., language editing, code generation, figure creation), and which sections were affected, sufficient for a reader to assess the AI's role.
AI as author
The disallowed practice of listing a generative AI system (e.g., ChatGPT, Claude) in the author byline or contributor list of a scholarly work, on the rationale that AI cannot meet authorship criteria requiring accountability, agreement, and the capacity to take public responsibility.
AI-generated content
Text, images, code, or other artefacts produced substantively by a generative AI system in response to a prompt, where the AI is the proximate source of the content rather than a tool refining human-authored material.
AI-assisted writing
The use of a generative AI tool by a human author to draft, edit, paraphrase, summarise, or stylistically revise text where the human retains final editorial control and authorship responsibility.
