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

Research output: Contribution to journalArticle (Academic Journal)

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


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 Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 151 KB, PDF document



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