I'm not sure if Pinterest posted this article with some other intent (like having more support programs), but in it's current state, it seems discriminatory.
There actually are things driving men out of the field. Look at how many head nurses are male. Men in nursing is a problem as well. But this isn't NurseNews, it's HackerNews.
Umm, in the actual example we are talking about, the evidence is on the side of that assumption being false: https://news.ycombinator.com/item?id=9977751 https://jaymans.wordpress.com/jaymans-race-inheritance-and-i...
In fact, this depends on the underlying population sizes of tall vs. short people, so this is a faulty argument.
More germane to the situation, however, is your initial assumption, whose analogue is dubious. So much so, in fact, that it is taboo to even allude to it. cf. pg's "What you can't say".
I'm not completely sold on the method, but the problem of subconscious bias in hiring is very well documented:
* Resumes with stereotypically White names receive 50% more callbacks than identical resumes with traditionally black ones. The addition of honors and special skills had a significant effect on the likelihood of White applications being called, but a statistically insignificant effect for the otherwise-identical African-American ones. http://www.nber.org/papers/w9873.pdf
* Applicants with male names are rated as more competent and offered higher starting salaries than identical applications with female names. Identical resumes with male names are called back more often than female ones. http://www.pnas.org/content/109/41/16474.full.pdf+html, http://advance.cornell.edu/documents/ImpactofGender.pdf
* Initial subconscious bias is logically justified post-hoc—in the linked experiment, the qualities deemed necessary for a position would actually be redefined by the person reviewing resumes depending on the gender of the applicants so that the male applicant's traits fit the position better. http://www.socialjudgments.com/docs/Uhlmann%20and%20Cohen%20...
There's quite a few more papers on the subject, it's easy to search for them. Depressing read all around.
1) ask people not include their names on their resume. Or don't use resumes at all. Create a web form application that does not require a name and only asks about the crucial skills you actually need.
2) Create a highly objective hiring process, such as this: http://sockpuppet.org/blog/2015/03/06/the-hiring-post/
I don't really buy that subconscious bias is the problem in tech. I've been part of a lot of hiring, we were actually consciously biased toward hiring women since we wanted better numbers. I had an objective interview process, always giving the same questions, and the hiring was still very disproportionately male whasian (the applicant pool was also very disproportionately male whasian, despite doing active reach out to woman-in-tech events).
2. Also agree that an objective method would be preferable, thanks for the link. Great read. IIRC, in the study I link in the third bullet point, forcing the person reviewing the resume to create a rubric before reading any of them greatly reduced the biased results. Having an objective process goes a long way. The point in the link about confident interviewers performing better in the traditional hiring process is definitely true, and also tied in a roundabout way to gender, funnily enough (women tend to be less confident than men: http://www.theatlantic.com/features/archive/2014/04/the-conf...).
It's definitely not the only problem—the pipeline is obviously skewed dramatically—but I don't see any reason why tech workers would be exempt from the biases of the general population as they've been studied. Maybe the drive to hire more women would counteract it, like in your example—that's the reasoning under which minority quotas are implemented, after all. The problem of subconscious bias is that it's not consciously recognizable by its very nature. It's just gut feeling. I know a site that uses association to try to test it for you, if you're interested: http://implicit.harvard.edu/.
The questions I asked were representative of things we actually had to do on the job, for instance: "write code to spider a web site." Does that unfairly weed out woman?
I personally find tangible monetary value in employees with diverse backgrounds but I don't lower my bar. I just slightly discount sameness and slightly appreciate difference.
Person 1 says that enforcing a 50 people of type A and 50 people of type B when the number of qualified candidates is 20 and 80 will result in either hiring significantly fewer people in general, or hiring under qualified people of type A. People of type B are being passed over because of their race/gender/ethnicity. This is discrimination.
Person 2 says that implying that anyone of type A could be under qualified is itself discrimination, and this possibility should not be addressed or considered. Nothing can change because the issue cannot be discussed.
We have a tendency to assume type A is qualified and type B isn't. That is a natural bias based on evidence. We must actively subdue our biases if we want to overcome them, sometimes with rules.
I think we sometimes forget that the goal isn't to get a qualified employee but a successful employee. Qualifications generally leads to success but not always, so we should constantly question our qualification criteria.
White males have benefited from discrimination for a long time.
White males have been discriminated against sometimes.
This article isn't about discrimination or affirmative action.
It's about setting a goal to be more diverse, not by lowering the bar, or forced hiring, but through awareness, inclusion, and an opportunity to interview.
BTW, everyone's been given a chance they didn't deserve.
Your interest in the content they host has zero relevance to the technical challenges they face as a platform and heavily trafficked website.
Unfortunately, biases are rarely so blatant. Many are even non-malicious and entirely unconscious, like how people tend to guess older ages for black youth than white youth. http://www.apa.org/news/press/releases/2014/03/black-boys-ol...
I still say they don't represent the technical community. And it's blatantly obvious they don't represent the HN community, given the downvotes.
I'm not saying there aren't biases, sexism, ageism, or racism in tech, but I'd appreciate it if you didn't point at the obvious troll and say "this right here is the face of tech".
Women and brown people are not lower quality humans, and it has been shown over and over and over that companies who hire for diversity perform better.
1. Their approach seems like it will work, if it does, mostly by increasing the chances that women engineers or women engineering students will choose to work for Pinterest instead of work for someone else.
2. I've frequently read that there is a shortage of tech workers, and companies like Pinterest have trouble finding the people they need.
3. Putting this together, does this mean that much of any increase in diversity they get will come at the expense of reducing the diversity elsewhere (unless, of course, they significantly hire away from other companies that are more diverse than they are)?
A bit of Googling turns up assorted claims on what percent of developers are women, but many seem to claim in the 10-20% range.
It would be interesting to take the data from Pinterest, and from other tech companies where this kind of data is available, and use that to classify them into three groups: those where women are under represented, those where they are over represented, and those where it is neither. The comparison should be to the percentage of women in tech, not the percentage in the general population.
It would then be interesting to see if there is something the companies in each group have in common.
The possibility of that being true is certainly unsettling for me because I've always assumed that hiring by merit is intrinsically superior to hiring by quota.
And much respect to Tracy for moving the needle.
But of course the minds of men and women are not identical, and recent reviews of sex differences have converged on some reliable differences. Sometimes the differences are large, with only slight overlap in the bell curves.
With some other traits the differences are small on average but can be large at the extremes. That happens for two reasons. When two bell curves partly overlap, the farther out along the tail you go, the larger the discrepancies between the groups. For example, men on average are taller than women, and the discrepancy is greater for more extreme values. At a height of five foot ten, men outnumber women by a ratio of thirty to one; at a height of six feet, men outnumber women by a ratio of two thousand to one. Also, confirming an expectation from evolutionary psychology, for many traits the bell curve for males is flatter and wider than the curve for females. That is, there are proportionally more males at the extremes. Along the left tail of the curve, one finds that boys are far more likely to be dyslexic, learning disabled, attention deficient, emotionally disturbed, and mentally retarded (at least for some types of retardation).
At the right tail, one finds that in a sample of talented students who score above 700 (out of 800) on the mathematics section of the Scholastic Assessment Test, boys outnumber girls by thirteen to one, even though the scores of boys and girls are similar within the bulk of the curve [NOTE this was from the pre-1994 SAT where the math section was harder and not truncated at the top. Today the ratio is a bit less than 2-1 at the top end.]. With still other traits, the average values for the two sexes differ by smaller amounts and in different directions for different traits. Though men, on average, are better at mentally rotating objects and maps, women are better at remembering landmarks and the positions of objects. Men are better throwers; women are more dexterous. Men are better at solving mathematical word problems, women at mathematical calculation. Women are more sensitive to sounds and smells, have better depth perception, match shapes faster, and are much better at reading facial expressions and body language. Women are better spellers, retrieve words more fluently, and have a better memory for verbal material.
Nonetheless, discussions of the leaky pipeline in science rarely even mention an alternative to the theory of barriers and bias. One of the rare exceptions was a sidebar to a 2000 story in Science, which quoted from a presentation at the National Academy of Engineering by the social scientist Patti Hausman: "The question of why more women don’t choose careers in engineering has a rather obvious answer: Because they don’t want to. Wherever you go, you will find females far less likely than males to see what is so fascinating about ohms, carburetors, or quarks. Reinventing the curriculum will not make me more interested in learning how my dishwasher works."
An eminent woman engineer in the audience immediately denounced her analysis as “pseudoscience.” But Linda Gottfredson, an expert in the literature on vocational preferences, pointed out that Hausman had the data on her side: “On average, women are more interested in dealing with people and men with things.” Vocational tests also show that boys are more interested in “realistic,” “theoretical,” and “investigative” pursuits, and girls more interested in “artistic” and “social” pursuits.
The most dramatic example comes from an analysis by David Lubinski and Camilla Benbow of a sample of mathematically precocious seventh-graders selected in a nationwide talent search. The teenagers were born during the second wave of feminism, were encouraged by their parents to develop their talents (all were sent to summer programs in math and science), and were fully aware of their ability to achieve. But the gifted girls told the researchers that they were more interested in people, “social values,” and humanitarian and altruistic goals, whereas the gifted boys said they were more interested in things, “theoretical values,” and abstract intellectual inquiry. In college, the young women chose a broad range of courses in the humanities, arts, and sciences, whereas the boys were geeks who stuck to math and science. And sure enough, fewer than 1 percent of the young women pursued doctorates in math, physical sciences, or engineering, whereas 8 percent of the young men did. The women went into medicine, law, the humanities, and biology instead.
Gottfredson points out, “If you insist on using gender parity as your measure of social justice, it means you will have to keep many men and women out of the work they like best and push them into work they don’t like.” She is echoed by Kleinfeld on the leaky pipeline in science: “We should not be sending [gifted] women the messages that they are less worthy human beings, less valuable to our civilization, lazy or low in status, if they choose to be teachers rather than mathematicians, journalists rather than physicists, lawyers rather than engineers.” These are not hypothetical worries: a recent survey by the National Science Foundation found that many more women than men say they majored in science, mathematics, or engineering under pressure from teachers or family members rather than to pursue their own aspirations— and that many eventually switched out for that reason. I will give the final word to Margaret Mead, who, despite being wrong in her early career about the malleability of gender, was surely right when she said, “If we are to achieve a richer culture, rich in contrasting values, we must recognize the whole gamut of human potentialities, and so weave a less arbitrary social fabric, one in which each diverse human gift will find a fitting place.”
When you combine the imbalance in high-end math test scores, with the imbalance of innate preference for abstract work versus people work, it is not at all surprising that only 20% of Pinterest would be female.
If Pinterest feels like its hiring practice is not meritocratic, it should fix that.
If Pinterest feels like it is missing out on an untapped candidate pool, it should figure out how to tap that pool.
But there is zero reason to try to make the female ratio 30% for the sake of making it 30%. There is no moral reason, there is no practical reason. Males and females are different, twas ever thus. This obsession with equalizing employment number in all spheres is a bizarre (and destructive) fashion of our age.
Maybe because I'm not American the part that confuses me is the "Hispanic" vs "Latino", are they used to mean different things?
Technically (very technically), Latin-American would include people talking French or Portuguese, while I guess Hispanic will only include Spanish speakers, e.g. not including Brazil. But I don't know if they are used in a different way in the US.
That would greatly reduce the usability of the charts in this case. The charts are much more useful when they all present the categories in the same order.
If only it was a joke...