Update. #AI researchers are among those pissed when #PeerReview of their work is outsourced to AI.
https://www.chronicle.com/article/ai-scientists-have-a-problem-ai-bots-are-reviewing-their-work
(#paywalled)

One complained, “If I wanted to know what #ChatGPT thought of our paper, I could have asked myself.”

Update. New study: "The majority of human reviewers’ comments (78.5 %) lacked equivalents in #ChatGPT's comments."
https://www.sciencedirect.com/science/article/abs/pii/S0169260724003067

#AI #LLM #PeerReview

Update. This editorial sketches a fantasy of #AI-assisted #PeerReview, then argues that it's "not far-fetched".
https://www.nature.com/articles/s41551-024-01228-0

PS: I call it far-fetched. And you?

Update. The @CenterforOpenScience (#COS) and partners are starting a new project (Scaling Machine Assessments of Research Trustworthiness, #SMART) in which researchers voluntarily submit papers to both human and #AI reviewers, and then give feedback on the reviews. The project is now calling for volunteers.
https://www.cos.io/smart-prototyping

#PeerReview

Update. "Researchers should not be using tools like #ChatGPT to automatically peer review papers, warned organizers of top #AI conferences and academic publishers…Some researchers, however, might argue that AI should automate peer reviews since it performs quite well and can make academics more productive."
https://www.semafor.com/article/05/08/2024/researchers-warned-against-using-ai-to-peer-review-academic-papers

#PeerReview

Update. "We demonstrate how increased availability and access to #AI technologies through recent emergence of chatbots may be misused to write or conceal plagiarized peer-reviews."
https://link.springer.com/article/10.1007/s11192-024-04960-1

#PeerReview

Update. "Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these [#CS] conferences could have been substantially modified by #LLMs, i.e. beyond spell-checking or minor writing updates."
https://arxiv.org/abs/2403.07183

#AI #PeerReview

Update. 𝘓𝘢𝘯𝘤𝘦𝘵 𝘐𝘯𝘧𝘦𝘤𝘵𝘪𝘰𝘶𝘴 𝘋𝘪𝘴𝘦𝘢𝘴𝘦𝘴 on why it does not permit #AI in #PeerReview:
https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(24)00160-9/fulltext

1. In an experimental peer review report, #ChatGPT "made up statistical feedback and non-existent references."

2. "Peer review is confidential, and privacy and proprietary rights cannot be guaranteed if reviewers upload parts of an article or their report to an #LLM."

Update. "Avg scores from multiple ChatGPT-4 rounds seems more effective than individual scores…If my weakest articles are removed… correlation with avg scores…falls below statistical significance, suggesting that [it] struggles to make fine-grained evaluations…Overall, ChatGPT [should not] be trusted for…formal or informal research quality evaluation…This is the first pub'd attempt at post-publication expert review accuracy testing for ChatGPT."
https://arxiv.org/abs/2402.05519

#AI #PeerReview

Update. If you *want* to use #AI for #PeerReview:

"Several publishers…have barred researchers from uploading manuscripts…[to] #AI platforms to produce #PeerReview reports, over fears that the work might be fed back into an #LLM’s training data set [&] breach contractual terms to keep work confidential…[But with] privately hosted [and #OpenSource] LLMs…one can be confident that data are not fed back to the firms that host LLMs in the cloud."
https://www.nature.com/articles/d41586-023-03144-w

Update. The US #NIH and Australian Research Council (#ARC) have banned the use of #AI tools for the #PeerReview of grant proposals. The #NSF is studying the question.
https://www.science.org/content/article/science-funding-agencies-say-no-using-ai-peer-review
(#paywalled)

Apart from #quality, one concern is #confidentiality. If grant proposals become part of a tool's training data, there's no telling (in the NIH's words) “where data are being sent, saved, viewed, or used in the future.”

#Funders

Update. I'm pulling a few other comments into this thread, in preparation for extending it later.

1. I have mixed feelings on #attribution in peer review. I see the benefits, but I also see the benefits of #anonymity.
https://twitter.com/petersuber/status/1412455826397204487

2. For #AI today, good #reviews are a harder problem than good #summaries.
https://fediscience.org/@petersuber/109954904433171308

3. Truth detection is a deep, hard problem. Automating it is even harder.
https://fediscience.org/@petersuber/109921214854932516

#PeerReview #OpenPeerReview

Update. I acknowledge that there's no bright line between using these tools to polish one's language and using them to shape one's judgments of quality. I also ack that these tools are steadily getting better at "knowing the field". That's why this is a hard problem.

One way to ensure that reviewers take #responsibility for their judgments is #attribution.

#PeerReview #OpenPeerReview

Good start on a hard question — how or whether to use #AI tools in #PeerReview.
https://www.researchsquare.com/article/rs-2587766/v1

"For the moment, we recommend that if #LLMs are used to write scholarly reviews, reviewers should disclose their use and accept full responsibility for their reports’ accuracy, tone, reasoning and originality."

PS: "For the moment" these tools can help reviewers string words together, not judge quality. We have good reasons to seek evaluative comments from human experts.