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Conference peer review constitutes a human-computation process whoseimportance cannot be overstated: not only it identifies the best submissionsfor acceptance, but, ultimately, it impacts the future of the whole researcharea by promoting some ideas and restraining others. A surge in the number ofsubmissions received by leading AI conferences has challenged thesustainability of the review process by increasing the burden on the pool ofqualified reviewers which is growing at a much slower rate. In this work, weconsider the problem of reviewer recruiting with a focus on the scarcity ofqualified reviewers in large conferences. Specifically, we design a procedurefor (i) recruiting reviewers from the population not typically covered by majorconferences and (ii) guiding them through the reviewing pipeline. Inconjunction with ICML 2020 -- a large, top-tier machine learning conference --we recruit a small set of reviewers through our procedure and compare theirperformance with the general population of ICML reviewers. Our experimentreveals that a combination of the recruiting and guiding mechanisms allows fora principled enhancement of the reviewer pool and results in reviews ofsuperior quality compared to the conventional pool of reviews as evaluated bysenior members of the program committee (meta-reviewers).

A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences