AI Article Reviews Dominate Human Peer Reviews at Their Best: Evidence and Policy Choices
Gary King, Steven Worthington. 2026.
"AI Article Reviews Dominate Human Peer Reviews at Their Best: Evidence and Policy Choices".
Abstract
We show that AI-generated article reviews are now better, by every measure we could construct, than reviews written by well-compensated human experts and judged to be of unusually high quality by journal editors and authors. We then discuss how this result may inform journal policy choices, with concerning downsides as well as potential paths to increase publication speed and quality; reduce adversarial interactions; improve the experience of editors, reviewers, authors, and readers; and advance science.
See Also
- [Paper] Where's the Evidence that Respondents Understand Your Survey Questions? (2026)
- [Paper] Experimental Evidence on the (Limited) Influence of Reputable Media Outlets (2025)
- [Paper] Global Maternal Health Country Typologies: A Framework to Guide Policy (2024)
- [Presentation] Public Policy for the Poor? A Randomized Evaluation of the Mexican Universal Health Insurance Program (Harvard School of Public Health) (2022)
- [Paper] Survey Data and Human Computation for Improved Flu Tracking (2021)
- [Paper] Do Nonpartisan Programmatic Policies Have Partisan Electoral Effects? Evidence from Two Large Scale Experiments (2020)
- [Presentation] Empowering Social Science to Understand and Ameliorate Major Challenges of Human Society (Federal Interagency Conference on Social Science and Big Data) (2020)
- [Presentation] Statistically Valid Inferences from Privacy Protected Data (Interagency Arctic Research Policy Committee) (2020)