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

Research output: Contribution to journalArticle (Academic Journal)peer-review

51 Citations (Scopus)
400 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)70-79
Number of pages10
JournalCommunications of the ACM
Volume60
Issue number3
Early online date21 Feb 2017
DOIs
Publication statusPublished - 1 Mar 2017

Structured keywords

  • Jean Golding

Keywords

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

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