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I think the industry is suffering from the cobra effect right now https://en.wikipedia.org/wiki/Cobra_effect
This guy won't win any prizes for marketing.
If the stereotype for a senior engineer is a balding white male with glasses, and you have two prospective engineers with otherwise identical resumes and work histories: one balding white male with glasses and even a pocket protector on his white shirt, and one black woman with a wild T-shirt, which would you hire? The answer I'd heard before was that you should hire the non-conformist, because they've risen to identical positions and accomplishments despite the bias against them at each step, while the stereotypical engineer was probably assumed to belong in the positions they attained. So you should choose the stuttering surgeon and the messy lawyer when given the opportunity.
(No idea where I heard it or what the phenomenon is called, though - anyone remember this?)
Creating a throwaway since I've taken part of this internal discussion at Facebook. Once normalized for skill level (i.e. junior engineer, engineer, senior engineer) the level of rejection between genders is the same.
Earlier quoted context omitted.
Women are more likely to get hired to solve the unequal distribution of female/male engineers. This makes it more likely to have less qualified female engineers, and more qualified male engineers, since hiring requirements for men are more strict. A higher average of under qualified females compared to under qualified male engineers will lead to more code getting reject from the female counterpart.
This is also a biased assumption that women are worse developers... seems a little quick to jump to that conclusion.
In reality, the pool of female talent is very shallow. Again assuming the talent levels are exactly the same, there's just not enough highly-skilled women to go around, e.g. if you're trying to hire the top 1% of engineers, and you have a pool of 1 million men and 100,000 women, then you're looking at 10000 men and 1000 women as your potential hires. If the top 1000 women are already taken, then you're forced to relax your interview requirements for women. In fact, if the pool is completely free for your usage, but you're looking to hire 10000 men and 10000 women, you'll be hiring the top 1% of male talent and down to the top 10% of female talent, which should lead to a difference in output.
This doesn't mean that "sexism" isn't real or that it's not a problem women face in the workplace. The question however, is how to measure the effectiveness of "anti-sexism" practices employed, if you cannot measure the effect of "sexism" itself, as it is entangled with all the other, confounding factors. You can't optimize performance if you can't measure the effects your solutions have on it.
I've heard that the opposite of this suggested bias ought to be consciously corrected. If the stereotype for a senior engineer is a balding white male with glasses, and you have two prospective engineers with otherwise identical resumes and work histories: one balding white male with glasses and even a pocket protector on his white shirt, and one black woman with a wild T-shirt, which would you hire? The answer I'd h…
Taleb - Surgeons Should Not Look Like Surgeons
Earlier quoted context omitted.
Women are more likely to get hired to solve the unequal distribution of female/male engineers. This makes it more likely to have less qualified female engineers, and more qualified male engineers, since hiring requirements for men are more strict. A higher average of under qualified females compared to under qualified male engineers will lead to more code getting reject from the female counterpart.
This is also a biased assumption that women are worse developers... seems a little quick to jump to that conclusion.
Imagine they did the same thing for university entrance: men need all prerequisite courses and a 3.8gpa, whereas women need only 75% of prereqs and a 2.9gpa. It only makes sense that the women are going to score lower in exams/flunk out, since their initial starting point was, on average, further behind.
Earlier quoted context omitted.
Care to elaborate on how the cobra effect relates here?
Facebook took heat for not having a 50/50 male/female workforce, when that is not the ratio of male to female devs available. They hire more females, based on the fact they are female. The code quality of some of these hires was not good, because they were not hired on merit, and they've created a code quality problem.