Computational support for academic peer review
a perspective from artificial intelligence
Citation
Price, S & Flach, P, 2017, ‘Computational support for academic peer review: a perspective from artificial intelligence’. Communications of the ACM, vol 60., pp. 70-79
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.
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