It seems more than a little quixotic if fixing every known flaw in the hiring process isn't enough - that the process must get a certain hiring outcome in order to be "fair".
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.
But "we're somehow secretly and unknowingly discriminating in our hiring process" doesn't need such justification before using it to make decisions?
For example: does any part of your interview involve a subjective evaluation? If so, do any of the evaluators know the candidates' names? If so, congratulations, your process is probably biased! http://www.politifact.com/punditfact/statements/2015/mar/15/...
That's literally all it takes: some human judgment and some way to guess at the applicant's race. The discrimination can happen subconsciously. You and I would probably fall victim to it despite our best intentions.
Since the manifesto we're discussing is mostly about gender, it's worth mentioning that the same thing happens there: http://m.pnas.org/content/109/41/16474.abstract
Nobody knows how to screen job applicants for pure merit without bias. If it's a choice between "our process is imperfect just like everybody else's" and "we stumbled upon the perfect unbiased screening process and all bias in the output is due to inherent variation in the population," bet on the first one every time. It's about a million times more likely.
There's an underlying assumption in these discussions that implementing a meritocracy is easy and examining every candidate without regard to their race and sex is the default state of affairs. In this view, any changes you make to the process is necessarily de-optimizing for merit.
But what is "merit"? This isn't a field where you can quantify it. If you were hiring people to lift heavy objects or something, you could test them all and hire the ones with the best numbers and be confident you got the best people for the job. There's no way to implement something like that for the jobs Google has.
So we approximate. We look at degrees and open source projects and do in-person interviews. We pretend that this results in a purely objective evaluation of the candidate's ability to do the job, free of any bias... not because there's any reason to think that's true, but because we really want it to be.
Techies often bemoan whiteboard interviews for being unfair: the skill being tested isn't really the skill you'd use on the job, and it discriminates against people who get nervous when put on the spot, or whatever. It's not outrageous to think that maybe some parts of the interview process, without ever intending to, discriminate against women and minorities in a similar way.