03 · What You Need to Know
How Generative AI Can Affect the Authenticity of Scholarly Peer Review
What Is a Fake Peer-Reviewer Report?
A fake peer-reviewer report is a purported scholarly assessment that misrepresents the identity, independence, authorization, or actual conduct of the reviewer.
For example, a report may be submitted through an impersonated reviewer account, produced under a fictitious identity, or arranged through an unauthorized process designed to influence editorial decisions.
The report may contain genuine-looking scientific commentary while lacking the independent evaluation represented to the editor.
However, not every inadequate or inaccurate review is fake. Genuine reviewers sometimes misunderstand methods, overlook important limitations, or submit superficial comments.
Review quality and review authenticity are related but distinct concerns.
How Is AI-Assisted Peer Review Different From Fraudulent Peer Review?
Some reviewers may use AI tools for tasks such as language editing, organizing notes, or identifying issues to examine. Whether such assistance is permitted depends on the journal's policies and the confidentiality obligations associated with the manuscript.
Fraudulent review involves deception about the reviewer, the independence of the assessment, or the process through which it was submitted.
Authorized AI assistance
A genuine reviewer uses AI within applicable editorial and confidentiality requirements while retaining responsibility for the assessment.
Unauthorized or undisclosed AI use
A genuine reviewer uses AI contrary to relevant journal requirements. This may constitute a policy violation without necessarily involving a fake reviewer identity.
Fraudulent peer review
A report is deceptively represented as an authorized independent assessment, potentially involving impersonation, fictitious identities, or coordinated manipulation.
These situations should not be treated as interchangeable. A review can be genuine but violate an AI-use policy, while a fraudulent review can be written entirely by a human.
What Can Generative AI Produce in a Reviewer Report?
Generative AI can imitate several conventional components of peer-review reports.
| Review component |
Possible AI-generated content |
Verification concern |
| Manuscript summary |
A description of the research question and claimed contribution |
Whether the summary accurately reflects the manuscript |
| Methodological assessment |
Comments about sampling, design, instruments, or analysis |
Whether the criticism is technically justified |
| Statistical evaluation |
Statements about model choice, significance, or robustness |
Whether the underlying calculations were actually examined |
| Literature assessment |
Claims about originality and previous scholarship |
Whether relevant sources were consulted and verified |
| Revision recommendations |
Requests for additional analyses or textual changes |
Whether recommendations address genuine weaknesses |
| Editorial recommendation |
Accept, revise, or reject recommendations |
Whether an authorized reviewer made an independent judgment |
The ability to generate these components does not establish that the system has performed a sufficiently rigorous scientific review.
Why Can AI-Generated Reviewer Reports Sound Credible?
Peer-review reports often follow recognizable conventions. Reviewers summarize the manuscript, discuss its strengths, identify methodological limitations, and recommend revisions.
Generative AI can reproduce these conventions using fluent academic language.
A report may therefore sound balanced and authoritative while containing generic observations that could apply to many manuscripts.
For example, recommending a clearer theoretical framework or a more detailed limitations section may be reasonable without demonstrating that the reviewer critically examined the particular study.
Researchers and editors should distinguish persuasive language from manuscript-specific expertise.
Detailed wording alone is not evidence of authentic peer review.
Can AI Produce Methodological Criticism Without Understanding the Study?
Generative AI can identify common methodological concerns, but the relevance of its criticism depends on the information available and the correctness of its interpretation.
For example, a system might recommend individual randomization in a study that appropriately uses a cluster-randomized design.
It may also criticize the absence of inter-rater agreement in a qualitative study where that measure is not required by the chosen analytical tradition.
Such comments can appear technically sophisticated while reflecting inappropriate assumptions.
Editors should assess whether the report engages with the actual design, data, and disciplinary standards.
A convincing methodological vocabulary is not a substitute for justified scientific reasoning.
Can AI Generate a Reviewer Report Without Reading the Manuscript?
A generative system can produce generic reviewer comments from a title, abstract, or brief topic description.
These comments may include plausible recommendations about literature coverage, sampling, statistical reporting, and limitations.
However, they cannot establish that the complete manuscript was critically examined.
Even when the full manuscript is supplied, an AI-generated assessment may overlook important problems or invent weaknesses that are not present.
The completeness of the input and the validity of the resulting comments require separate consideration.
Editors should not assume that a lengthy report demonstrates comprehensive review.
How Might AI-Generated Reports Facilitate Reviewer Impersonation?
Reviewer impersonation occurs when someone falsely represents themselves as another person in the editorial process.
Generative AI may make it easier to produce reviewer-style content for an unauthorized account, but the deceptive identity is a separate element of the misconduct.
A report's language cannot establish whether the named reviewer actually submitted it.
Verification may require independently established professional contact information and authorized editorial records.
The principles governing AI detection of fake peer reviews emphasize the importance of identity and process verification.
Editors should avoid assuming that a professional-looking report came from the individual named in the reviewer account.
Can AI Contribute to Coordinated Peer-Review Manipulation?
Coordinated peer-review manipulation may involve several accounts or individuals acting to influence editorial decisions deceptively.
Generative AI could potentially reduce the effort required to produce multiple reviewer-style reports with different wording.
However, identifying coordinated manipulation requires evidence about relationships among submissions, accounts, communications, and editorial activity.
Similar language alone does not establish coordination.
Legitimate reviewers may use common templates or disciplinary expressions.
Likewise, stylistically different reports may still originate from coordinated fraudulent activity.
Editors should focus on corroborated process evidence rather than linguistic impressions.
Why Are Author-Suggested Reviewers Relevant?
Some journals invite authors to recommend qualified reviewers.
This practice can help editors identify specialists, particularly in narrow research areas.
However, reviewer suggestions require appropriate editorial verification because the submitting authors should not control the independence of the review process.
Editors may need to confirm reviewer identity, expertise, and potential conflicts of interest through sources independent of the manuscript's authors.
An author-suggested reviewer is not automatically suspicious.
The concern arises when the identity or independence of the proposed reviewer is misrepresented.
Can AI Produce Fabricated Reviewer Identities?
Generative AI can produce plausible names, affiliations, professional descriptions, and contact information.
However, a plausible profile does not establish that the person exists or is associated with the claimed institution.
Editors should verify reviewer identities through appropriate independent sources.
At the same time, the absence of a prominent online profile does not establish that a researcher is fictitious.
Early-career scholars, independent researchers, and academics from less comprehensively indexed institutions may have limited public visibility.
Verification should be evidence-based rather than dependent on institutional prestige or digital prominence.
Can AI-Generated Reviews Be Used to Promote Acceptance?
A fabricated report may recommend acceptance while minimizing substantive criticism.
Generative AI can produce favorable assessments that resemble conventional reviewer language.
However, a positive recommendation does not establish fraud. Genuine reviewers may reasonably conclude that a manuscript requires only minor revisions.
Editors should examine whether the report provides an adequate assessment and whether its provenance is authentic.
A report's recommendation and its legitimacy are separate questions.
Can AI Generate Convincing Negative Reviews?
Yes. Generative AI can also produce critical reports recommending rejection.
Such reports may contain detailed objections, including unsupported claims about methodological flaws or missing literature.
Consequently, fraudulent review should not be associated exclusively with favorable recommendations.
Manipulation could theoretically involve attempts to disadvantage competing work as well as promote a particular manuscript.
However, allegations of such conduct require evidence rather than assumptions about the reviewer's motives.
Editors should evaluate both favorable and unfavorable reviews for authenticity and substantive quality.
Can AI-Text Detectors Identify Fake Reviewer Reports?
AI-text detectors attempt to classify writing according to features associated with machine-generated language.
These methods may produce false positives and false negatives, particularly when text is edited or generated using unfamiliar systems.
Even an accurate determination that AI contributed to a report does not establish whether the reviewer identity is genuine or whether the use violated journal policy.
Conversely, a fraudulent review may contain entirely human-written text.
AI-writing detection therefore cannot independently establish peer-review fraud.
Review authenticity requires evidence about authorization and editorial conduct.
Why Is Confidentiality a Major Concern?
Peer reviewers frequently receive unpublished manuscripts under confidentiality obligations.
Uploading manuscript content to an external generative AI service may expose unpublished findings, confidential participant information, or proprietary research details.
Some journal and publisher policies restrict or prohibit such use.
Reviewers should consult the applicable policy before processing manuscript material through AI tools.
Even when a service offers privacy protections, its use must be authorized for the relevant content.
The convenience of AI-generated commentary does not override reviewer confidentiality obligations.
Who Is Accountable for an AI-Assisted Review?
A genuine reviewer remains responsible for the assessment submitted under their identity.
AI assistance does not transfer scholarly or ethical responsibility to the system.
The reviewer should verify the accuracy of substantive claims, ensure that recommendations reflect independent judgment, and comply with applicable policies.
Journal editors remain responsible for editorial decisions.
These responsibilities are consistent with the broader principles of transparency and accountability described in the European Code of Conduct for Research Integrity.
What Happens When Peer Review Is Compromised?
Compromised peer review may undermine confidence in the process through which a manuscript was evaluated.
However, identifying a fraudulent review does not automatically establish that the underlying research data are fabricated.
The scientific reliability of the manuscript and the integrity of its review process require related but distinct assessments.
Depending on the circumstances and applicable policies, editorial responses may include additional independent review, correction of the publication record, or retraction.
COPE provides guidance for handling peer-review manipulation and related publication-integrity concerns.
Any response should be based on verified evidence rather than an AI-generated suspicion score.
How Should Editors Investigate a Suspicious Reviewer Report?
The first step is to identify the specific reason for concern.
A report may be unusually brief, submitted rapidly, linguistically repetitive, or associated with inconsistent identity information.
These indicators should be evaluated separately.
Editors may verify the reviewer's identity using independently established contact details, examine authorized editorial records, and assess the substantive quality of the report.
Where credible evidence of manipulation remains, the journal should follow its established integrity procedures.
The distinction between AI misconduct flags and formal investigations remains important.
How Can Journals Prevent Fake AI-Generated Reviews?
Prevention depends partly on reviewer selection and identity verification.
Journals should maintain appropriate controls over reviewer invitations, account access, conflicts of interest, and editorial decision-making.
AI may assist with identifying unusual patterns, but it should not independently authenticate reviewers based on opaque scores.
Editors should also evaluate whether reviews provide meaningful manuscript-specific assessment.
However, an inadequate report should not automatically be characterized as fraudulent.
Procedural safeguards are likely to be more dependable than relying exclusively on linguistic detection.
Why Should Detection Systems Be Evaluated for False Positives?
Incorrectly identifying a genuine reviewer as fraudulent may damage professional reputation and disrupt editorial processes.
False-positive risks may be especially significant when detectors rely on writing style, review duration, or unfamiliar email domains.
These features can have legitimate explanations.
Institutions should evaluate whether screening methods perform adequately across relevant reviewer populations and publication contexts.
The risks of AI-generated false accusations of research misconduct apply to reviewer allegations as well as manuscript screening.
Can Peer-Review Fraud Be Detected Reliably From the Report Alone?
Not generally. A reviewer report contains information about its content, but may provide limited evidence about the identity and authorization of the person who submitted it.
Textual patterns can support screening, yet a sophisticated fraudulent report may appear indistinguishable from an ordinary review on linguistic grounds.
Likewise, a genuine report may contain repetitive or generic language.
Reliable assessment often requires information from the editorial process rather than the report text alone.
Reviewers should avoid inferring provenance from style without corroborating evidence.
What Should Researchers Do If They Suspect Their Manuscript Received a Fake Review?
Authors may raise specific concerns through the journal's established communication or appeal procedures.
For example, they may identify apparent conflicts of interest, inconsistent reviewer comments, or other verifiable issues.
However, disagreement with a recommendation does not establish that the review was fraudulent.
Editors should evaluate credible concerns without unnecessarily disclosing confidential reviewer information.
The objective is to preserve independent scholarly assessment while ensuring that allegations about reviewers are handled fairly.
Watch Out
Do not equate AI-generated reviewer language with fraudulent peer review. A genuine reviewer may use permitted AI assistance, while a fake review may be entirely human-written. Verify identity, authorization, independence, and compliance with editorial policies.
