One big difference between reviewing for conferences like NIPS/ICML and ISIT is that there is a “discussion” period between the reviewers and the Area Chair. These discussions are not anonymized, so you know who the other reviewers are and you can also read their reviews. This leads to a little privacy problem — A and B may be reviewing the same paper P, but A may be an author on a paper Q which is also being reviewed by B. Because A will have access to the text of B’s reviews on P and Q, they can (often) unmask B’s authorship of the review on Q simply by looking at the formatting of the reviews (are bullet points dashes or asterisks, do they give numbered points, are there “sections” to the review, etc). This seems to violate the spirit of anonymous review, which is perhaps why some have suggested that reviewing be unblinded (at least after acceptance).

The extent to which all of this matter is of course a product of the how fast the machine learning literature has grown and the highly competitive nature of the “top tier conferences.” Because the acceptance rate is so low, the reviewing process can appear to be “arbitrary” (read: subjective) and so questions of both review quality and author/review anonymity impact possible biases. However, if aim of double-blind reviewing is to reduce bias, then shouldn’t the discussions also be anonymized?