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

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.