Is That Reliable and Feasible for AI as a Peer Reviewer?
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Abstract
Peer review is the cornerstone of scientific quality control, yet it faces growing challenges including reviewer fatigue, bias, inconsistency, and escalating submission volumes. Artificial intelligence has recently been proposed as a potential tool—or even substitute—for human peer reviewers. This review article critically examines whether AI can reliably and feasibly function as a peer reviewer in scholarly publishing. Drawing on developments in natural language processing, machine learning, and automated evaluation systems, the article analyzes current capabilities, limitations, ethical concerns, and structural constraints. Rather than asking whether AI can replace human reviewers outright, this review evaluates where AI meaningfully contributes to review processes and where human judgment remains indispensable. The analysis suggests that AI shows promise in technical screening, methodological consistency checks, and bias reduction, but remains limited in conceptual novelty assessment, epistemic judgment, and ethical reasoning. Ultimately, AI is better positioned as a co-reviewer or decision-support system rather than an autonomous arbiter of scientific merit.
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Artificial Intelligence, Peer Review, Gamma-Aminobutyric Acid, Research Integrity, Automation
No funding source declared.
Birhane, A., Prabhu, V., & Kahembwe, E. (2021). Multimodal datasets: Misogyny, pornography, and malignant stereotypes. arXiv. https://arxiv.org/abs/2110.01963
Blank, R. M. (1991). The effects of double-blind versus single-blind reviewing: Experimental evidence from The American Economic Review. American Economic Review, 81(5), 1041–1067.
Bouter, L. M. (2018). Fostering responsible research practices is a shared responsibility of multiple stakeholders. Journal of Clinical Epidemiology, 96, 143–146. DOI: https://doi.org/10.1016/j.jclinepi.2017.12.016
COSE (Council of Science Editors). (2018). Ethics in scientific publishing (2nd ed.). University of Chicago Press.
Gerstner, K., & König, J. (2023). Artificial intelligence in peer review: Opportunities, risks, and governance challenges. Research Integrity and Peer Review, 8, 18. DOI: https://doi.org/10.1186/s41073-023-00133-7
Horbach, S. P. J. M., & Halffman, W. (2018). The changing forms and expectations of peer review. Research Integrity and Peer Review, 3, 8. DOI: https://doi.org/10.1186/s41073-018-0051-5
Kovanis, M., Porcher, R., Ravaud, P., & Trinquart, L. (2016). The global burden of journal peer review in the biomedical literature: Strong imbalance in the collective enterprise. PLOS ONE, 11(11), e0166387. DOI: https://doi.org/10.1371/journal.pone.0166387
Lipworth, W., Kerridge, I., & Carter, S. M. (2011). Journal peer review in context: A critical review of the literature. Health Sociology Review, 20(3), 297–314.DOI: https://doi.org/10.5172/hesr.2011.20.3.297
Marcus, G., & Davis, E. (2019). Rebooting AI: Building artificial intelligence we can trust. Pantheon Books.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. DOI: https://doi.org/10.1126/science.aax2342
Resnik, D. B., Gutierrez-Ford, C., & Peddada, S. (2008). Perceptions of ethical problems with scientific journal peer review: An exploratory study. Science and Engineering Ethics, 14(3), 305–310. DOI: https://doi.org/10.1007/s11948-008-9059-4
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