I've been reading http://hunch.net/
for their take on machine learning articles but they've been posting some nice essays recently on the underlying frameworks for reviewing papers, etc.
Reviewing is a fairly formal process which is integral to the way academia is run. Given this integral nature, the quality of reviewing is often frustrating. I’ve seen plenty of examples of false statements, misbeliefs, reading what isn’t written, etc…, and I’m sure many other people have as well.
Recently, mechanisms like double blind review and author feedback have been introduced to try to make the process more fair and accurate in many machine learning (and related) conferences. My personal experience is that these mechanisms help, especially the author feedback. Nevertheless, some problems remain.
The game theory take on reviewing is that the incentive for truthful reviewing isn’t there. Since reviewers are also authors, there are sometimes perverse incentives created and acted upon. (Incidentially, these incentives can be both positive and negative.)
Setting up a truthful reviewing system is tricky because their is no final reference truth available in any acceptable (say: subyear) timespan. There are several ways we could try to get around this.