[yummyfajitas]

After studying this issue, and learning a lot more about learning and optimization, I've come to the conclusion that the best solution [1] is probably explicit racial/sexual/other special interest group quotas.

Specifically, we should train a classifier on non-Asian minorities. We should train a different classifier on everyone else. Then we should fill our quotas from the non-Asian minority pool and draw from the primary pool for the rest of the students.

As this blog post describes, no matter what you do you'll reduce accuracy. But every other fairness method I've seen reduces accuracy both across special interest groups and also within them. Quotas at least give you the best non-Asian minorities and also the best white/Asian students.

Quotas also have the benefit of being simple and transparent - any average Joe can figure out exactly what "fair" means, and it's also pretty transparent that some groups won't perform as well as others and why. In contrast, most of the more complex solutions obscure this fact.

[1] Here "best" is within the framework of requiring a corporatist spoils system. I don't actually favor such a system, but I'm taking the existence of such a spoils system as given.

[h4nkoslo]

The problem is that you run out of "good" NAMs (or women with the exact same career preferences as men, etc.) extremely quickly. The demand for "good" NAMs in any given field vastly exceeds supply, since quotas tend to be set at population proportion.