📽️ [Video] How Editors Find Reviewers: The Hidden Crisis Behind Peer Review

Finding reviewers used to sound straightforward: identify two or three experts, invite them, and wait for their reports. Today, it has become one of the hardest parts of an editor’s job.

After reviewing more than 450 scientific manuscripts and handling editorial decisions across several international journals, I see three forces reshaping reviewer selection: reviewer fatigue, artificial intelligence, and conflicts of interest.

And together, they create some uncomfortable paradoxes. Everyone wants fast peer review. Fewer people have time to review. Editors can spend days sending invitation after invitation before securing two reviewers. Some researchers decline. Many never respond. Others accept but struggle to meet the deadline.

Meanwhile, authors understandably ask why their manuscript has been “awaiting reviewer assignment” for weeks. The irony is difficult to ignore: we depend on other researchers to review our papers quickly, while the academic system gives us very little incentive to spend hours reviewing theirs.

Perhaps every researcher should occasionally ask: How many papers did I submit this year, and how many did I review?

Then there is AI. Editorial systems can increasingly suggest reviewers by analyzing keywords, publications, citation networks, research profiles, and manuscript similarity. This can help editors discover excellent specialists outside their own networks. But AI is entering the other side of the process too. Authors use AI to improve manuscripts. Editors use AI-assisted tools to identify reviewers. Reviewers increasingly use AI to analyze or draft reviews. Authors may then use AI again to prepare their responses. So we are approaching a strange situation:

AI finds the reviewer. AI helps write the review. AI helps answer the review. Who exactly is reviewing whom?

The issue is not whether AI should be prohibited. Used responsibly, it can be extremely useful. The real questions are about confidentiality, disclosure, accountability, and scientific judgment. An unpublished manuscript is confidential material. A reviewer cannot simply outsource scientific judgment to an algorithm and remain accountable only when the algorithm gets it right. Then comes perhaps the most interesting paradox of all.

The perfect reviewer may also be your closest competitor.

Editors want someone who understands the manuscript deeply. But in highly specialized fields, the people who understand your work best may be working on exactly the same problem. Too far from the topic, and the reviewer may miss the contribution. Too close, and conflicts, competition, or intellectual bias may appear. This is why reviewer selection is not simply a search for expertise. Editors must consider independence, collaborations, institutional relationships, previous reviewing behavior, publication activity, workload, and potential conflicts. Even apparently innocent reviewer comments can require editorial judgment.

“The authors should cite these five important papers.” Are those papers genuinely essential? What if four were written by the reviewer?

Editors do not simply evaluate manuscripts. Sometimes we also have to evaluate the reviewers evaluating the manuscript. That is why reviewer recommendations are advice, not votes. A researcher can be scientifically brilliant and still be the wrong reviewer. The person an editor ultimately wants is someone who combines four qualities:

Expertise + Independence + Availability + Reliability.

Finding all four in one person is becoming increasingly difficult. There is also an important lesson here for early-career researchers. Editors remember good reviewers.

If you accept reviews within your expertise, deliver them on time, identify the real scientific issues, avoid unnecessary demands, disclose conflicts, and write constructive reports, you gradually become someone editors trust. Keep your ORCID, Scopus, institutional profile, and research keywords current. Make your expertise discoverable. Editors cannot invite researchers they cannot find. Peer review ultimately depends on something remarkably fragile: scientists voluntarily giving their time to evaluate the work of other scientists. AI may make reviewer discovery faster. Better databases may improve matching. Editorial platforms may become smarter. But none of them solves the central problem.

How do we preserve rigorous human scientific judgment when submissions are increasing, reviewers are exhausted, experts may be competitors, and AI is entering every stage of the process?

That may be one of the defining challenges for the future of peer review.

Editors: How many invitations do you typically send before securing two reviewers?

Reviewers: What makes you accept or immediately decline an invitation?

📚 Part of the playlist: Reviewers & Editors: Roles and Responsibilities

🎥 Watch the video: 👉 https://www.youtube.com/watch?v=C5uPUzMOTEE

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