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

TL;DR

This paper introduces a new method for recruiting and guiding novice reviewers from underrepresented populations to address the scarcity of qualified reviewers in large AI conferences, demonstrating improved review quality.

Contribution

It proposes a novel reviewer recruitment and guidance procedure and empirically validates its effectiveness at ICML 2020, enhancing review quality with novice reviewers.

Findings

Recruitment and guidance improve review quality.

Novice reviewers can match or exceed experienced reviewers.

Method is effective in large conference settings.

Abstract

Conference peer review constitutes a human-computation process whose importance cannot be overstated: not only it identifies the best submissions for acceptance, but, ultimately, it impacts the future of the whole research area by promoting some ideas and restraining others. A surge in the number of submissions received by leading AI conferences has challenged the sustainability of the review process by increasing the burden on the pool of qualified reviewers which is growing at a much slower rate. In this work, we consider the problem of reviewer recruiting with a focus on the scarcity of qualified reviewers in large conferences. Specifically, we design a procedure for (i) recruiting reviewers from the population not typically covered by major conferences and (ii) guiding them through the reviewing pipeline. In conjunction with ICML 2020 -- a large, top-tier machine learning conference -- we recruit a small set of reviewers through our procedure and compare their performance with the general population of ICML reviewers. Our experiment reveals that a combination of the recruiting and guiding mechanisms allows for a principled enhancement of the reviewer pool and results in reviews of superior quality compared to the conventional pool of reviews as evaluated by senior members of the program committee (meta-reviewers).