Editors’ note: Today’s post is by Elena Vicario, Director of Research Integrity at Frontiers.

Recent discussions on papermills and industrialized fraud have made one point undeniable: AI has become a powerful accelerant of research misconduct at scale. That reality is uncomfortable, but it is one the publishing sector needs to face directly. Investigations by Nature and Science into industrialized fake paper production have highlighted the scale and sophistication of the problem.

Organized fraud at this scale and sophistication demands a response that is equally systematic, and that includes the responsible use of AI. Basically, if AI is part of the problem, it also has to be part of the answer. During the past decade in research integrity, I have seen the use of AI in misconduct rise sharply. At the same time, AI is being used constructively by scientists to produce research and by publishers to prevent fake science from entering the scientific record.

Publishers increasingly rely on AI-supported systems to identify image manipulation, plagiarism, paper mill indicators, citation anomalies, and other integrity risks before publication. The STM’s Integrity Hub is one example of how AI-supported integrity screening is already becoming embedded across publishing workflows. From a research integrity perspective, the question is no longer whether AI belongs in scholarly publishing. It already does. The question is how it should be governed.

Yet, one part of the workflow remains especially contested: peer review. That caution is understandable. Peer review involves confidential, unpublished material, and concerns about data security, misuse, and over-reliance on automated tools are real. But it is increasingly difficult to defend blanket restrictions on reviewer use of AI when AI is already embedded elsewhere in the publishing process. My question is simple: why block conscientious reviewers from responsible AI use when, on either side of them, AI is already thriving and becoming indispensable to the publishing cycle?

The problem is that policy is lagging behind how people actually work. Reviewers are already using AI tools quietly to support parts of their assessments, whether to summarize manuscripts, sense-check statistics, interrogate code, or improve language and structure. Rather than denying that reality, everyone needs clarity about what is acceptable, under what conditions, and with what safeguards. Blocking AI use on misconduct grounds is not just inconsistent; it is counterproductive. The same system that relies on AI to detect image manipulation, papermill patterns, plagiarism, and statistical anomalies at scale, then turns around and tells reviewers not to use similar tools. That does not remove AI from peer review. It simply pushes it under the radar, unregulated. And that is where the real risk sits.

Peer review is the backbone of the scientific record, but it is under strain. Submission volumes continue to rise, and reviewer capacity struggles to keep pace while expectations of scrutiny increase. IOP Publishing’s State of Peer Review 2024, drawing on responses from over 3,000 researchers, found growing reviewer fatigue and workload imbalance as persistent structural problems.

Reviewers need the right tools. Used properly, AI is invaluable in the reviewer toolkit. Used invisibly or inconsistently, it creates new fault lines.

This is not, at heart, a technology problem. It is a governance and coordination problem. Funders, universities, and publishers are all responding to AI, but in different ways and at different speeds. Some permit limited use, others prohibit it, and many stay silent or remain unclear, leaving reviewers to navigate the problem alone.

Meanwhile, experimentation with AI-supported review workflows is gathering pace, including proposals for AI-reviewed preprints and automated evaluation models in open research. Fragmented use and policy do not slow AI adoption. They just make it less consistent and less transparent.

This is where organizations like the Committee on Publication Ethics can play an important role. COPE has long helped define the ethical baseline for scholarly publishing. It has already begun engaging with AI through discussion documents and position statements relating to generative AI and publishing workflows. But a gap remains in providing reviewers themselves with clear, operational guidance on what they can and cannot do with AI. The sector is still operating in a grey zone. The sector is still operating in a grey zone. What is needed now is not simply more acknowledgement that AI exists, but clear, practical guidance that helps editors, reviewers, and publishers work to a shared standard.

That means moving beyond the blunt “allowed versus prohibited” framing, which no longer reflects reality. Instead, COPE and others should lead on a set of shared expectations that people can actually work with:

• Transparency: meaningful disclosure of AI use, without penalizing responsible behavior or driving it underground
• Accountability: human reviewers remain fully responsible for their judgments and peer review reports, regardless of which tools supported the work. Journals should make this explicit in reviewer agreements.
• Boundaries: clarity on where AI can support (summarization, language, structure) and where it cannot replace judgement on novelty, significance, or scientific rigor
• Confidentiality and security: unpublished manuscripts should not be uploaded to tools that do not meet appropriate privacy and data-handling standards
• Oversight: editorial processes that make AI use visible, reviewable and auditable, just as other declarations and conflicts are already managed within publishing workflows

None of this is theoretical. Reviewers and editors are already navigating these questions in real time, often without clear guidance. That is not a sustainable position for a system that depends on trust.

Wheels are in motion, including at COPE and across the publishing sector, with STM consultation about AI use disclosure discussed at the World Conference on Research Integrity (WCRI) 2026. This is welcome, but the sector now needs leadership capable of translating discussion into shared standards and operational policy. COPE is well placed to convene that process from an ethical and governance perspective, while STM, publishers, institutions and funders need to help define what practical implementation looks like across real publishing workflows.

The current consultations and discussions at WCRI provide an important opportunity to begin establishing consensus around the core principles already emerging across the sector: transparency, accountability, confidentiality, oversight, and clear boundaries around where AI can and cannot support peer review. Those discussions should not end as conference talking points. They should become the basis for practical guidance and harmonized expectations that journals, editors and reviewers can effectively apply.

If the sector does not act, the gap between policy and practice will continue to widen. AI use in peer review will not disappear; it will simply remain uneven, insufficiently visible, and governed by inconsistent rules. That is a risk to trust.

The task is not to treat AI as either a solution or a threat to peer review. It is to define where it can appropriately support peer review, where it must be constrained, and how human accountability is preserved throughout.

What is needed now is timely, practical guidance that reflects how peer review is actually evolving and helps the sector respond with consistency and confidence.