IIRC it was very clear: once you submitted the public test set and obtained a score that broke the 10% barrier there was a 30 day wait period for final submisions.

Then the ones over 10% were run against the final test set and the best winner determined from that.

Obviously releasing specific details about the final test set doesn't work because teams can fit to that :)

They don't need to make it public beforehand, but they really should after-the-fact, to verify their calculations upon it. Then, proving it's unbiased is a simple matter of releasing the hash to the dataset before the contest starts, to prove that the final dataset they release is the same one they generated before the contest started.

Now, on the front of selecting the winners before the contest has even started, and telling them about how to bias their algorithm so it will do better on the final--I can't really help you. I'd just hope that the programming teams would be honorable enough to mention if one of them were offered such a thing.

OK, I agree that more transparency would be nice, but I don't think it's a serious concern. It's clearly in Netflix's best interest to promote a fair competition, since the whole point of the exercise (the reason they're spending a million dollars) is to produce the best possible general algorithm for their own use.

Did you read the contest rules and FAQ? (http://www.netflixprize.com/rules, http://www.netflixprize.com/faq -- and if you didn't, what are you doing throwing around words like "corrupt"?) It looks to me like they're being very transparent and explained things very clearly. You have a bunch of data in the test set; these are partitioned randomly into two equally-sized subsets, "quiz" and "test". You submit predictions for both subsets, but are only told how you did on "quiz". Netflix provides an MD5 checksum for the judging file that defines what the partition is; this file will be made available "at the end of the Contest". So this will be verifiable by anyone soon.

Also, don't brush overfitting aside. In the (paraphrased) words of one machine learning researcher, "life is a battle against entropy. In the same way, machine learning research is a battle against overfitting." Any data whose test results you use to select your algorithm or to adjust your algorithm's parameters is not properly considered part of the test set; after your optimization that data will provide an optimistically-biased estimate of your true error. Since competitors could get regular updates on their performance on the quiz dataset, one must assume that they were attempting to optimize this performance, and so quiz set performance was not be a good estimate of their true error. You can only get a good estimate of the true error of a method by testing against data that has played no part in its development.