  Recent successes in the Machine Learning community have led to a steep
increase in the number of papers submitted to conferences. This increase made
more prominent some of the issues that affect the current review process used
by these conferences. The review process has several issues that may undermine
the nature of scientific research, which is of being fully objective,
apolitical, unbiased and free of misconduct (such as plagiarism, cheating,
improper influence, and other improprieties). In this work, we study the
problem of reviewers' recruitment, infringements of the double-blind process,
fraudulent behaviors, biases in numerical ratings, and the appendix phenomenon
(i.e., the fact that it is becoming more common to publish results in the
appendix section of a paper). For each of these problems, we provide a short
description and possible solutions. The goal of this work is to raise awareness
in the Machine Learning community regarding these issues.
