Apr 14, 2025 03:58 PM
Experts warn 'AI-written' paper is latest spin on climate change denial
https://techxplore.com/news/2025-04-expe...atest.html
EXCERPTS: Climate change deniers are pushing an AI-generated paper questioning human-induced warming, leading experts to warn against the rise of research that is inherently flawed but marketed as neutral and scrupulously logical.
[...] Academics have warned that the surge of AI in research, despite potential benefits, risks triggering an illusion of objectivity and insight in scientific research. "Large language models do not have the capacity to reason. They are statistical models predicting future words or phrases based on what they have been trained on. This is not research," argued Mark Neff, an environmental sciences professor.
The paper says Grok 3 "wrote the entire manuscript," with input from co-authors who "played a crucial role in guiding its development." (MORE - details)
Peer reviews of peer reviews: A randomized controlled trial and other experiments
https://journals.plos.org/plosone/articl...ne.0320444
ABSTRACT: Is it possible to reliably evaluate the quality of peer reviews? We study this question driven by two primary motivations – incentivizing high-quality reviewing using assessed quality of reviews and measuring changes to review quality in experiments.
We conduct a large scale study at the NeurIPS 2022 conference, a top-tier conference in machine learning, in which we invited (meta)-reviewers and authors to voluntarily evaluate reviews given to submitted papers.
First, we conduct a randomized controlled trial to examine bias due to the length of reviews. We generate elongated versions of reviews by adding substantial amounts of non-informative content. Participants in the control group evaluate the original reviews, whereas participants in the experimental group evaluate the artificially lengthened versions. We find that lengthened reviews are scored (statistically significantly) higher quality than the original reviews.
Additionally, in analysis of observational data we find that authors are positively biased towards reviews recommending acceptance of their own papers, even after controlling for confounders of review length, quality, and different numbers of papers per author. We also measure disagreement rates between multiple evaluations of the same review of 28% – 32%, which is comparable to that of paper reviewers at NeurIPS.
Further, we assess the amount of miscalibration of evaluators of reviews using a linear model of quality scores and find that it is similar to estimates of miscalibration of paper reviewers at NeurIPS. Finally, we estimate the amount of variability in subjective opinions around how to map individual criteria to overall scores of review quality and find that it is roughly the same as that in the review of papers.
Our results suggest that the various problems that exist in reviews of papers – inconsistency, bias towards irrelevant factors, miscalibration, subjectivity – also arise in reviewing of reviews... (MORE - details)
https://techxplore.com/news/2025-04-expe...atest.html
EXCERPTS: Climate change deniers are pushing an AI-generated paper questioning human-induced warming, leading experts to warn against the rise of research that is inherently flawed but marketed as neutral and scrupulously logical.
[...] Academics have warned that the surge of AI in research, despite potential benefits, risks triggering an illusion of objectivity and insight in scientific research. "Large language models do not have the capacity to reason. They are statistical models predicting future words or phrases based on what they have been trained on. This is not research," argued Mark Neff, an environmental sciences professor.
The paper says Grok 3 "wrote the entire manuscript," with input from co-authors who "played a crucial role in guiding its development." (MORE - details)
Peer reviews of peer reviews: A randomized controlled trial and other experiments
https://journals.plos.org/plosone/articl...ne.0320444
ABSTRACT: Is it possible to reliably evaluate the quality of peer reviews? We study this question driven by two primary motivations – incentivizing high-quality reviewing using assessed quality of reviews and measuring changes to review quality in experiments.
We conduct a large scale study at the NeurIPS 2022 conference, a top-tier conference in machine learning, in which we invited (meta)-reviewers and authors to voluntarily evaluate reviews given to submitted papers.
First, we conduct a randomized controlled trial to examine bias due to the length of reviews. We generate elongated versions of reviews by adding substantial amounts of non-informative content. Participants in the control group evaluate the original reviews, whereas participants in the experimental group evaluate the artificially lengthened versions. We find that lengthened reviews are scored (statistically significantly) higher quality than the original reviews.
Additionally, in analysis of observational data we find that authors are positively biased towards reviews recommending acceptance of their own papers, even after controlling for confounders of review length, quality, and different numbers of papers per author. We also measure disagreement rates between multiple evaluations of the same review of 28% – 32%, which is comparable to that of paper reviewers at NeurIPS.
Further, we assess the amount of miscalibration of evaluators of reviews using a linear model of quality scores and find that it is similar to estimates of miscalibration of paper reviewers at NeurIPS. Finally, we estimate the amount of variability in subjective opinions around how to map individual criteria to overall scores of review quality and find that it is roughly the same as that in the review of papers.
Our results suggest that the various problems that exist in reviews of papers – inconsistency, bias towards irrelevant factors, miscalibration, subjectivity – also arise in reviewing of reviews... (MORE - details)