  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).
