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Computational support for academic peer review: a perspective from artificial intelligence

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)70-79
Number of pages10
JournalCommunications of the ACM
Volume60
Issue number3
Early online date21 Feb 2017
DOIs
DateAccepted/In press - 25 Jul 2016
DateE-pub ahead of print - 21 Feb 2017
DatePublished (current) - 1 Mar 2017

Abstract

State-of-the-art tools from machine learning and artificial intelligence are making inroads to automate parts of the peer review process; however, many opportunities for further improvement remain.

Profiling, matching and open-world expert finding are key tasks that can be addressed using feature-based representations commonly used in machine learning.

Such streamlining tools also offer perspectives on how the peer review process might be improved: in particular, the idea of profiling naturally leads to a view of peer review being aimed at finding the best publication venue (if any) for a submitted paper.

Creating a more global embedding for the peer review process which transcends individual conferences or conference series by means of persistent reviewer and author profiles is key, in our opinion, to a more robust and less arbitrary peer review process.

    Structured keywords

  • Jean Golding

    Research areas

  • Artificial Intelligence, Data Science, Machine Learning, Recommender systems, Expert finding, Peer review

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via ACM at http://dl.acm.org/citation.cfm?doid=3055102.2979672. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 151 KB, PDF document

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