Why not "women are just not as interested in math, logic and computer science to pursue it AS OFTEN as men"? Why are you not considering this possibility?
Ah, the Damore argument. Besides the fact that his psuedo science has been summarily handled[0], to consider his argument you then have to equally consider the possibility of sexism in academia pressuring women to not study these subjects and societal pressure their whole lives pressuring them to not persue these career paths.
There's also the idea that lack of women scientist "heroes" can be limiting (lack of role models). Basically the idea that if you stack the cards against a population, you're gonna see population-wide effects.
Given these data points, a biased hiring AI contributes to the problem. Therefore, it should be fixed, along with the above points.
The rhetorical context here is that human children look for other human adults that they could potentially grow into in order to aim their own dreams and hopes for their adulthood. If a young human boy sees an adult human man pursuing computers, the young human boy learns that being interested in computers is a socially viable construct and this will affect how he pursues his interests in the future. In consequence, if a young human girl does not see any adult human women in computers, she may not understand that that option is available to her and this will affect how she pursues her interests. Although there is some fuzziness in determining this (some children grow up to be trailblazers, others pursue passions regardless of examples).
Wouldn't being an outcast make you even more attracted to heroes of your "outcast class?" Because, presumably, the hero had to overcome so much more for society to recognize them.
Depending on the era, we had Einstein, Turing, feinman. Kids my age had Gates (literally the richest man on the planet for my entire formative years), Jobs, Bill Nye. Little further along are the myth busters crew, musk...
True or false isn't necessarily something I think you could say in debates about human genetics, yet.
For now I say it was "handled" in that not only did he fail to demonstrate that female disinterest in engineering, compared to male, is due to inherent psychological differences, and I quoted a couple people far more qualified than me that reached the same conclusion (their statements are in the article. The Wikipedia page is another good summary)
Notably, Damore makes pretty much the same arguments against using race in hiring as he does gender, but failed to provide any proof for his arguments, he only really gave what he interpreted as evidence for his gender beliefs. There's little to disprove except for Damore's interpretation of results as being proof for his argument.
Population wide effects MLK dreamed about too. Reality is a different story. I think the whole end goal is misguided and is going to lead to a whole lot of frustration and disappointment and divisiveness.
Helping individuals to overcome biology is much simpler than doing it at population scale.
Referencing D. Schmitts article referenced in the BBC article, he's quoted as saying
>"that using someone's sex to work out what you think their personality will be like is "like surgically operating with an axe"."
Being phrased by the article as a dismissal of Damore, along with G. Rippon's statements However in the article Schmitt is quoted from, he writes that
>"Culturally universal sex differences in personal values and certain cognitive abilities are a bit larger in size (see here), and sex differences in occupational interests are quite large. It seems likely these culturally universal and biologically-linked sex differences play some role in the gendered hiring patterns of Google employees. For instance, in 2013, 18% of bachelor's degrees in computing were earned by women, and about 20% of Google technological jobs are currently held by women."
He goes on to write that Pyschological sex differences might lead to less than 50% of technology employees being women.
This seems to disagree with Professor Rippon's opinion that
>"but even if you accepted the idea that there are some biological differences, all researchers would assert that they're so tiny that there's no way that they can explain the kind of gender gap that's apparent at Google."
I think there's reason to consider both the societal reasons women might be pressured and excluded from STEM-ey fields, as well as potential inherent differences in interest, and that they can both coexist as considerations, and agree that a biased AI is unhelpful, and many women lack a fair shot of success, however disagree that there is nothing useful in Damore's perspective.
Additionally if such inherent differences are distributed on a bell curve, it would make sense that at cases further along the trail that small differences in populations and their medians are more pronounced.
The jump here in the data being displayed is that it is making a correlation between sex differences and bachelor's degree demographics. Very little in that rhetoric actually has logical sense such that we know that we are missing(usually) at least close to two decades of cultural and social conditioning before the bachelors degree. That's plenty of time to systematically condition women against specific fields.
One way to try and get around that issue may be to compare cultures with high Gender Equality Index scores or some similar metric versus those with lower scores but otherwise similar. Presumably the closer to parity those years before university are the more some other difference, if any, would be suggested.
Well... why is it, then, that underrepresentation of women must suggest sexism, but the (orders of magnitude higher) overrepresentation of asians - specifically from India - doesn't suggest bias?
From a recruiter standpoint, that answer is easy - there are literally orders of magnitude more Indians applying.
So the bias against women due to decades of societal conditioning leads to less than 50/50 representation because less are applying, which companies are trying to patch by leveling the playing field, making their internal population breakdown identical to the external one.
Seeing shitloads of Indians is a passive effect of that internal/external thing - there are around 1.2 billion Indians...
If these women are applying for tech jobs at Amazon, they're by definition interested. The uninterested (male or female) are not relevant to this discussion.
I must say, I am frustrated by this being brought up in every discussion of women & STEM. Want to discuss the leaky pipeline from physics PhDs to full professor in physics? "Maybe those women did a PhD in physics despite not being interested, and they just didn't notice before!"
That's always the excuse but an algorithm that shows bias against women has nothing to do with who has what interest unless that's a variable included in the data set initially. You are inferring that relation implicitly due to the result of the algorithm, but it doesn't mean that value is measured in the original set of data. If the algorithm is skewed to imply that, there is at least the possiblity that the algorithm has been trained to yield that result.
I wouldn't know unless I looked at all the data. But I'm not going to default to the popular opinion because that's literally half or more of the problem.
I haven't seen anything suggesting women are less interested, but there is some support for the idea that before college girls who are interested in math., etc., are more likely than boys to have other subjects that interest them more.
There was a study published a while back that looked at PISA data and found that girls and boys were pretty evenly represented among the kids who were at the top in STEM [1].
But it also found that for the boys in that group quite often STEM was the only thing they were outstanding at. In other areas they were average to good.
For the girls, on the other hand, they were often excellent at something else in addition to STEM, with them often even being better at that something else than they were in STEM.
People have a tendency to pursue a career in one of the areas they are very good at.
This suggests that boys who are very good in math, etc., are more likely than similarly good girls to pursue it as a career because that is their only choice if they want to go into something they are very good at. The girls are more likely to have math, etc., as one of two or more possible careers in areas they are very good at.
In pop culture terms, STEM boys are more like Martin Prince, and STEM girls are more like Lisa Simpson.
[1] I didn't save the link and have failed to find it with Google. Anyone have it?
Truth over the belief of who is actually 'the best fit' for a job doesn't exist until the job is complete. This isn't about oppression. It's about discrimination magically becoming automated because no one bothers to look for these things pre-deployment.
There's also the idea that lack of women scientist "heroes" can be limiting (lack of role models). Basically the idea that if you stack the cards against a population, you're gonna see population-wide effects.
Given these data points, a biased hiring AI contributes to the problem. Therefore, it should be fixed, along with the above points.
[0]https://www.bbc.co.uk/news/world-40865261
[0]https://www.theguardian.com/technology/2017/aug/13/james-dam...