> Failing a test will cost a user 600 points, or roughly the equivalent of 15 minutes of work on the platform. A correctly tuned penalty system removes the need for setting reviewer accuracy minimums; poor performers will simply not earn enough money to continue on the platform.
This still sets a reviewer accuracy minimum, but it is determined implicitly by the arbitrary test penalty instead of consciously chosen based on application requirements. I don't see how that's an improvement. If you absolutely want to have negative earnings, it would make more sense to choose a reviewer accuracy minimum to aim for, and then determine the penalty that would achieve that target, instead of the other way around.
Moreover, a reviewer earning nothing on expectation under this scheme (they work for 15 minutes, then fail a test, and have all their earnings wiped out) could team up with a second reviewer with the same problem, submitting their answer only when both agree, and as long as their errors aren't 100% correlated, they would end up with positive expected earnings they could split between them.
This clearly indicates that the incentive scheme as designed doesn't capture the full economic value of even lower-quality data when processed appropriately. Of course you can't expect random reviewers to spontaneously work together in this way, so it's up to the data consumer to combine the work of multiple reviewers as appropriate.
Trying to get reliable results from humans by exclusively hiring the most reliable ones can only get you so far; you can do much better by designing systems to use redundancy to correct errors when they inevitably do appear. Ironically, this is a case where treating humans as fallible cogs in a big machine would be more respectful.