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

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posted on 12.03.2018, 10:50 by Maciej J. Mrowinski, Piotr Fronczak, Agata Fronczak, Marcel Ausloos, Olgica Nedic
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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Citation

PLoS One, 2017, 12 (9), e0184711

Author affiliation

/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/School of Management

Version

VoR (Version of Record)

Published in

PLoS One

Publisher

Public Library of Science (PLoS)

eissn

1932-6203

Acceptance date

29/08/2017

Copyright date

2017

Available date

12/03/2018

Publisher version

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184711

Language

en

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