Artificial intelligence in peer review: How can evolutionary computation support journal editors?

Article, Preprint English OPEN
Maciej J Mrowinski ; Piotr Fronczak ; Agata Fronczak ; Marcel Ausloos ; Olgica Nedic
  • Publisher: Public Library of Science (PLoS)
  • Journal: PLoS ONE, volume 12, issue 9 (issn: 1932-6203, eissn: 1932-6203)
  • Related identifiers: pmc: PMC5607159, doi: 10.1371/journal.pone.0184711
  • Subject: Applied Mathematics | Algorithms | Science and Technology Workforce | Research Article | Mathematics | Evolutionary Algorithms | Professions | Computer Science - Digital Libraries | Careers in Research | Mathematical and Statistical Techniques | Simulation and Modeling | Population Groupings | Physical Sciences | Optimization | People and Places | Research Assessment | Crystallographic Techniques | Phase Determination | Science Policy | Computer Science - Neural and Evolutionary Computing | Mathematical Functions | Physics - Physics and Society | Research and Analysis Methods | Medicine | Evolutionary Computation | Q | R | Peer Review | Scientists | Science | Computational Techniques

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors’ workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.
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