Does Google's hiring practices "effectively lower the bar for 'diversity' candidates"? That's a key issue. If so, the details of Google's hiring practices themselves impugn the qualifications of preferred candidate groups.
The "lowering the bar" argument assumes there are not enough women or people of color in the population at large who are qualified for any given job. Google's approach probably assumes there are, and puts a burden on the hiring manager and recruiter to find and consider those candidates.
I don't work at Google and am wholly unfamiliar with their internal hiring practices. But in order to prove the author's specific "lowering the bar" claim, we'd need to see data not just on hiring numbers, but on success of the hired cohorts over time. (I'm willing to guess that Google does not try to hire unqualified candidates, period.)
"Lowering the bar" is a dangerous claim that offends the dignity and qualifications of every diverse employee at the company or ever hired by the company. A claim that bold would demand extraordinary evidence, which the author does not convincingly supply, and which I am taking a wild guess does not exist.
Of course, because of cultural and historical prejudices and biases, the pool of candidates that even apply to google is biased towards white males. Everything possible should be done to attract applications or seek out potential candidates from minority populations. But without suggesting any biological differences, it is still likely that because of cultural and historical prejudice, the distribution of talent among candidates will be skewed toward white males. Again, not because of any biological difference, but because they are more likely to have been encouraged or gone to rich universities or had parents who were engineers.
If we have a pool of 100 people and we want to hire the best five, then the bar is set by ranking the people by talent and qualification. If we want to hire everyone who is qualified, then wherever we set the bar, it is still likely that the population above the bar is skewed. So if we want to achieve a rebalancing or quota of employees from a certain sub-population, then it certainly does involve lowering the bar. We can argue over whether that is a good thing to want, or a fair thing, but I don't think you can logically argue that the bar isn't being lowered if you want to achieve a quota for a certain subpopulation. If you are suggesting that the population above the bar is not skewed, then you are suggesting that the terrible cultural stereotypes, biases and prejudices against women and minorities in technical fields has had no effect on their ability to achieve talent and qualification above the level of the bar, which I find implausible.
No, the argument is that there are not enough women or people of color in the population at large who are qualified for any given job _in the quantities desired by Google and every other tech company_.
They are trying to create a workforce with roughly proportional amounts of women, men, blacks, etc. when men are awarded ~80% of CS degrees and blacks are awarded, what, 2% or so? You can't move the needle in employee demographics when that's the pool you're working with unless you fudge your system a bit. Hell, given how universities already do this[1], why wouldn't Google?
[1]: http://www.latimes.com/local/california/la-me-adv-asian-race...
> Lee's next slide shows three columns of numbers from a Princeton University study that tried to measure how race and ethnicity affect admissions by using SAT scores as a benchmark. It uses the term “bonus” to describe how many extra SAT points an applicant's race is worth. She points to the first column.
African Americans received a “bonus” of 230 points, Lee says.
She points to the second column.
“Hispanics received a bonus of 185 points.”
The last column draws gasps.
Asian Americans, Lee says, are penalized by 50 points — in other words, they had to do that much better to win admission.
If this isn't the case and Google does actually hire people based on their skill and not skin color or gender, then Google needs to emphasize that, or else the majority of people will look upon women and such as "diversity hires" and build a disdain for them, thus hurting the cause.
Let's not Purity Test people for showing some humbleness in their beliefs. We could probably use more of that these days.
"Google doesn't think it has lowered the bar" is implied, not apparent.
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.
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.
He uses the phrase "effectively lower the bar" to mean something very different from what almost all of us think of as lowering the bar. The full quote is that one of Google's "discriminatory practices" is "Hiring practices which can effectively lower the bar for 'diversity' candidates by decreasing the false negative rate."
That is, the bar isn't lowered, they're just trying to fail to hire qualified candidates (from certain groups) less than they currently do.
Unless we have a reason to believe that false negatives are strongly correlated with the axis on which "the bar" is placed (and I don't think we do; false negatives are generally going to come from asking an out-of-left-field question, or the interviewee having a bad day, or something, which are pretty random), this doesn't lower the bar, "effectively" or otherwise.
I don't know that I'd agree with the second part; one thing you can reasonably conclude is correlated with false negatives is diversity (there are studies that people form hire/no-hire opinions within seconds of meeting a candidate, and studies that people have immediate biases when meeting people based on race/gender; there's also the simple fact that's put to good use in small companies that hiring people like yourselves, although mostly in ideological bias etc., is a good means for team cohesion). I assume Google has done more research on this than I have. Handing out "more tickets" may just be a way to cancel out the fact that so-called diversity candidates are getting worse tickets.
However, even if they were simply giving out more tickets to diversity candidates, the average level of qualification of these candidates doesn't change. You just have more of them. Maybe that's a problem because you're discriminating in their favor, but it certainly does not "effectively lower the bar" or impugn the qualifications of this over-represented group. They're all qualified. They're all on the right side of the "bar". There's no alleged increase in the false positive rate.
The fact that he chose to interpret this as "effectively lower[ing] the bar" is not just insulting to his colleagues and unprofessional, but also a sign that he doesn't understand math enough to pass a well-tuned Google interview and got one of the rare false-positive lottery tickets.
I don't think that works. If the process is optimised for accuracy, then decreasing the false negative rate should increase the the false positive rate, right? Intuitively that's how it would work for most classification tasks. If you can decrease the false negative rate without increasing the false positive rate then you're improving accuracy. The bar must be correlated with the false negative rate.
How large this effect is depends on just how noisy the first test is and on how widely above the bar people's abilities range.