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. To aid assigning submitted papers to reviewers a short list of subject keywords is often required by mainstream CMS tools as part of the submission process, either from a controlled vocabulary, such as the ACM Computing Classification System (CCS),a or as a free-text folksonomy. When a pair of keywords does not literally match, despite having been chosen to refer to the same underlying concept, one technique often used to improve matching is to also match their synonyms or syntactic variants. Publishers and bibliographic databases like DBLP and Google Scholar have developed their own proprietary UID schemes for identifying contributors to published works. However, there is now considerable momentum behind the non-proprietary Open Researcher and Contributor ID (ORCID)e and publishers are increasingly mapping their own UIDs onto ORCID UIDs. Creating a more global embedding for the peer-review process that transcends individual conferences or conference series by means of persistent reviewer and author profiles is key to a more robust and less arbitrary peer-review process.