And if they do dig into the inner workings of the hiring process and the results are still not representative of the general population? Do you just assume you've somehow managed to eliminate all bias and the results must reflect the population? Or do you assume that you haven't managed to 100% debug this highly difficult process?

It is very difficult to tell the difference between biased hiring and actual skew in the population. But when it's a trait that has no obvious connection to the job, it seems best to assume the process is biased, unless really good evidence exists to demonstrate a connection.

For example, let's say your interview process produces hires whose heights are substantially above average. If you're hiring for a basketball team then this would make perfect sense. If you're hiring Java programmers, it's highly likely that your interview process is screwed up. It's possible that the population of good Java programmers is unusually tall because of some biological factor, but this idea needs some major justification before you use it to make decisions.

Obviously, the first thing to do is to examine your interview process and eliminate any source of height bias you can find. But what if you do that and you're still hiring an abnormally large number of tall people? You could take this as sufficient evidence that tall people are better at this, but that's putting an awful lot of confidence in your interview program. It's more likely that you just haven't found all the sources of tall bias. If that's the case, then pushing for more short hires will improve the overall quality of your hires because you won't be artificially excluding good short ones.