  Modern machine learning and computer science conferences are experiencing a
surge in the number of submissions that challenges the quality of peer review
as the number of competent reviewers is growing at a much slower rate. To curb
this trend and reduce the burden on reviewers, several conferences have started
encouraging or even requiring authors to declare the previous submission
history of their papers. Such initiatives have been met with skepticism among
authors, who raise the concern about a potential bias in reviewers'
recommendations induced by this information. In this work, we investigate
whether reviewers exhibit a bias caused by the knowledge that the submission
under review was previously rejected at a similar venue, focusing on a
population of novice reviewers who constitute a large fraction of the reviewer
pool in leading machine learning and computer science conferences. We design
and conduct a randomized controlled trial closely replicating the relevant
components of the peer-review pipeline with $133$ reviewers (master's, junior
PhD students, and recent graduates of top US universities) writing reviews for
$19$ papers. The analysis reveals that reviewers indeed become negatively
biased when they receive a signal about paper being a resubmission, giving
almost 1 point lower overall score on a 10-point Likert item ($\Delta = -0.78,
\ 95\% \ \text{CI} = [-1.30, -0.24]$) than reviewers who do not receive such a
signal. Looking at specific criteria scores (originality, quality, clarity and
significance), we observe that novice reviewers tend to underrate quality the
most.
