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AI “ghosts” are undisclosed or uncredited AI systems whose generated wording, citations, analyses, images, data, or peer-review work enters the scholarly record without a clearly accountable human. They are not authors: authorship carries responsibility for accuracy, integrity, conflicts of interest, copyright, permissions, and licensing, duties an AI system cannot perform. The practical remedy is transparent disclosure, rigorous human checking, and strict protection of confidential material. A fluent paragraph is not evidence of reliable scholarship; every claim, reference, figure, and interpretation still needs a named person who can verify it and answer for it.
Policies differ in detail, but major publishers converge on the same boundary: limited assistance such as language polishing may be acceptable when disclosed and supervised, while fabricated evidence, concealed substantive generation, and delegated peer review are integrity violations.
What are “AI ghosts” in scholarly publishing?
An AI ghost is a system whose contribution is hidden while its output is treated as if it came from an accountable researcher. The hidden contribution may be a sentence, a literature summary, a statistical interpretation, a reference list, a figure, a synthetic image, a dataset, or a peer-review report. The problem is not that software was used; it is that readers cannot tell what the system did, which human checked it, or who accepts responsibility for errors.
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Where the ghosts enter
- False authority: polished language can conceal factual mistakes, omissions, bias, or invented references.
- Attribution failure: generated text may reproduce source material without quotation, citation, or permission.
- Synthetic evidence: fabricated data, results, figures, images, or citations can become part of the permanent record.
- Method opacity: undisclosed prompts, model versions, or AI-generated reasoning make replication and accountability harder.
- Confidentiality leakage: uploading a privileged manuscript or review to an unsecured service can breach obligations to authors and reviewers.
- Image-rights uncertainty: generated images can have unresolved accuracy, attribution, copyright, and privacy problems.
Why is ghost authorship an accountability problem?
Authorship is more than credit. It identifies people who can explain the methods, provide underlying material, disclose conflicts, correct the record, and accept consequences for misconduct. An AI system cannot consent to publication, hold a conflict of interest, guarantee that a citation supports a claim, obtain image permission, or respond to a correction or investigation.
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COPE’s 13 February 2023 guidance states plainly that “AI tools cannot be listed as an author of a paper.” Nature gave the same rationale on 24 January 2023: authorship entails accountability, and AI tools cannot take that responsibility. Listing a tool as an author, or hiding a substantial contribution so that no human can be questioned about it, therefore creates a responsibility gap even when the prose appears accurate.
How can AI contaminate the scholarly record?
Contamination happens when an output crosses from assistance into unverified evidence or untraceable reasoning. The following map links the common failure to the control an editor or author should apply.
| Failure mode | What a reader may see | Required control |
|---|---|---|
| Hallucinated or incomplete claims | A confident statement that no cited study supports, or a summary that omits contrary findings | Check the original source and the surrounding evidence; have a subject-matter author approve the wording |
| Fabricated citations | A plausible title, journal, DOI, page range, or author list that does not exist or does not support the sentence | Open every reference, verify bibliographic details, and confirm that it actually substantiates the claim |
| Plagiarism or unattributed reproduction | Distinctive wording copied from a source without quotation or permission | Run normal plagiarism and attribution checks; quote, cite, license, or rewrite as appropriate |
| Synthetic data or results | Numbers, tables, images, or statistical outcomes with no reproducible underlying work | Preserve raw data and analysis logs; never present generated material as observed evidence |
| Opaque analysis | Conclusions produced by an undisclosed prompt or model with no method a researcher can reproduce | Describe the tool, version, purpose, inputs, and human checks where the venue requires them |
| Confidentiality breach | Manuscript text or peer-review comments sent to a service that may retain or expose them | Follow the journal’s confidentiality rule and use only an approved, appropriately secured system |
| Unclear image provenance | A generated or altered figure whose subjects, training sources, rights, or alterations are unknown | Document origin and permissions, label permitted alterations, and follow the publication’s image policy |
Can ChatGPT or another AI be an author on a research paper?
No. COPE, Nature, ICMJE, Springer Nature, Nature Computational Science, and Elsevier all place authorship and accountability on human contributors. An AI tool may be named in a methods or acknowledgement statement when its use is permitted, but it cannot sign authorship declarations or take responsibility for the paper.
Do I have to disclose using AI to write my manuscript?
Usually, yes, whenever the tool did more than trivial spelling or interface assistance; the exact location and detail depend on the venue. The following comparison captures the requirements stated in the cited policies and guidance. Always apply the journal’s current instructions at submission.
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| Organization or policy | Permitted or expected use | Disclosure and accountability | Prohibited or high-risk conduct |
|---|---|---|---|
| COPE (reviewed 13 February 2023) | Tools may assist writing, images, data collection, or analysis when the use is transparent | Disclose how the tool was used; human authors remain liable for breaches | AI listed as an author or concealed use that obscures responsibility |
| Nature (24 January 2023) | Limited assistance may be used under the journal’s conditions | Document use in methods, acknowledgements, or another suitable section | Credited LLM authorship |
| ICMJE | Assistance can be used only with active human review | Humans must check accuracy, bias, plagiarism, attribution, permissions, and confidentiality; AI is not an author or cited author | Delegating those checks to the system |
| Springer Nature guidance | Language polishing, translation, structuring, and some analytical assistance may be allowed with disclosure and oversight | Explain the tool’s role and retain human responsibility | Fabricated data, citations, or results; delegated peer review; undisclosed core reasoning |
| Nature Computational Science (20 August 2026) | Use is considered when readers and reviewers can understand the AI’s role | Provide clear disclosure sufficient to evaluate that role | Fabricated data, images, or citations; AI authorship |
| Elsevier policy | Authors may receive assistance only within the policy’s limits | Verify accuracy, completeness, impartiality, and sources, and disclose use | Using AI output directly as manuscript text |
The policies are not interchangeable. One venue may permit translation with a statement, while another may require that generated prose be rewritten by the authors. A disclosure that satisfies one journal may be incomplete for another, so check the target publication immediately before submission.
What counts as acceptable AI assistance?
Acceptability turns on purpose, degree of generation, verification, and disclosure—not on the product name. Low-risk assistance generally changes presentation while leaving the research judgment with the authors.
Often acceptable when disclosed and checked
- Correcting grammar, spelling, punctuation, or readability in text the authors wrote.
- Translating a manuscript or preparing an accessible-language version, with a fluent human checking meaning and specialist terminology.
- Reorganizing headings or suggesting a clearer outline without inventing claims.
- Assisting a permitted analysis when the data, code, assumptions, and outputs are independently inspected and reproducible.
Normally unacceptable or high risk
- Inventing observations, participants, measurements, statistical results, references, quotations, or images.
- Using generated text as evidence without checking the underlying sources.
- Allowing an undisclosed system to supply the paper’s central reasoning or interpretation.
- Submitting a manuscript or review report to a service that the journal does not permit to receive confidential material.
- Asking AI to conduct peer review in place of the appointed reviewer or forwarding its comments as the reviewer’s own judgment.
Even a permitted use becomes unacceptable if the author cannot reconstruct what happened or cannot verify the output. “The model said so” is not a method, source, or quality-control step.
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Make the statement specific enough for a reader or editor to understand the system’s role and the human checks. Do not hide substantive generation behind a vague phrase such as “technology was used.”
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- Name the tool and version. Record the service or locally run model, release or model identifier, and provider when available.
- Record dates and purpose. State when it was used and whether the task was copyediting, translation, outlining, coding, analysis, image generation, or another activity.
- Describe the workflow. Identify what material was supplied, whether prompts or settings materially shaped the output, and which portions were retained.
- Explain verification. State that authors checked claims against primary sources, reran or inspected analyses, checked for plagiarism, and reviewed bias and omissions.
- Document images and permissions. Identify generated or altered figures, their provenance, and the permission or licensing basis required by the journal.
- Confirm confidentiality safeguards. Say whether any confidential manuscript, patient information, or peer-review material was submitted and what authorization covered it.
- Place the notice where required. Use the methods, acknowledgements, declaration, or other section specified by the venue; include a cover-letter explanation if requested.
A concise disclosure can be adapted to the journal’s format:
During manuscript preparation, the authors used [tool and version] on [dates] for [specific task]. Authors independently verified every factual statement, citation, calculation, interpretation, and image provenance, and revised the output. No confidential peer-review or unpublished third-party material was submitted without authorization.Remove any sentence that is untrue, and expand the statement when the journal asks for prompts, settings, or a fuller methods description.
What verification must a human perform before submission?
Human review is a substantive audit, not a quick read for tone. Assign named authors to the checks below and keep evidence of the checks for the project record.
- Claims: compare each factual assertion with the cited primary source, including qualifiers, dates, populations, and uncertainty.
- References: resolve every DOI or catalog record, inspect the cited passage, and delete references that cannot be verified.
- Reasoning and statistics: rerun code where possible, inspect assumptions and units, and ensure that conclusions do not exceed the data.
- Attribution: mark quotations, paraphrases, reused figures, and licensed material; obtain permission where required.
- Bias and completeness: look for missing counterevidence, demographic blind spots, and summaries that flatten disagreement.
- Images: preserve original files, note edits, verify that people and locations are represented accurately, and establish rights.
- Confidentiality: remove identifying details and confirm that every service used was permitted for the material supplied.
Can reviewers upload manuscripts to ChatGPT?
Not unless the journal explicitly permits that workflow and the service provides the confidentiality protections required by the review agreement. A peer-review manuscript is privileged material; sending it to an external system can expose unpublished ideas, personal data, or reviewer comments and may violate the promise made to the authors.
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Reviewers should first read the journal’s current reviewer instructions. If AI assistance is allowed, use only the approved environment, submit the minimum necessary text, remove identifiers when permitted, and disclose the tool and purpose in the review. Never paste a manuscript into a public or unknown system merely to obtain a summary, grammar edit, recommendation, or draft review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do journals detect AI-generated or contaminated papers?
Editors treat signals as leads for scrutiny, not automatic proof of misconduct. A generic voice, abrupt changes in style, duplicated phrasing, impossible references, irrelevant citations, or claims that cannot be traced to the cited literature can justify a request for source files and explanations. Image anomalies, missing raw data, inconsistent methods, and unexplained analytical steps can prompt similar checks.
A responsible investigation corroborates several kinds of evidence: reference verification, plagiarism and attribution checks, examination of data and code, image-provenance review, and questions to the authors about tools, versions, dates, prompts, and human validation. An AI detector score alone cannot establish who wrote a passage or whether the science is false; false positives and false negatives are possible. The outcome should rest on verifiable discrepancies and the venue’s integrity procedure.
What happens when AI fabricates data, images, results, or citations?
Fabrication is a serious research-integrity matter, whether a person or a model produced the false material. The editor may pause review, request raw records and an explanation, refer the case to the authors’ institutions, issue a correction or expression of concern, or retract the article. A disclosed tool does not excuse invented evidence, and an undisclosed tool does not by itself prove fabrication; the underlying record and the authors’ responsibility determine the response.
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How can an editorial team prevent AI ghosts?
Prevention works best when policy, submission forms, and human checks reinforce one another.
- Publish a plain-language rule defining allowed assistance, prohibited generation, image treatment, and peer-review confidentiality.
- Require an AI-use declaration that captures tool, version, date, purpose, inputs, outputs retained, and verification.
- Train editors and reviewers to recognize suspicious references and provenance gaps without treating style as proof.
- Ask for raw data, analysis code, original images, and permission records when the work depends on them.
- Escalate unexplained discrepancies through the journal’s research-integrity process rather than silently editing them away.
- Update the policy as model capabilities, publisher rules, privacy terms, and copyright decisions change.
The governing principle is simple: software may assist a human author, but it cannot replace the person who knows what was done, can show how it was checked, and will answer for the published record.
Frequently Asked Questions
Should researchers keep their AI prompts and chat logs?
Keeping prompts, model identifiers, dates, and retained outputs is prudent even when a journal does not require submission of the full log. The record helps authors reproduce a workflow, explain an unexpected passage, and respond if a publisher later asks how a result was produced.
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Can I use an AI grammar checker on a confidential accepted manuscript?
Only after checking the publisher’s confidentiality and data-processing rules. If permission is absent, edit locally or use a publisher-approved environment; acceptance does not make unpublished text public.
What should I do after discovering an invented citation in a submitted paper?
Tell the editor promptly, identify every affected claim, and supply corrected references or a withdrawal request if the error changes the findings. Do not quietly replace the citation while leaving the disclosure and audit trail unchanged.
Are AI-detection scores enough to reject a manuscript?
No. A score is a screening signal, not proof of authorship or fabrication. Editors should corroborate it with source checks, data and image review, and a documented opportunity for the authors to explain their workflow.
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