People do let them. You can't force what people are interested in and you can't let in that which does not exist. In fact many places in tech give preference to women applicants, because they don't apply often and the companies want more women. They're just rare to see. :(
There's no grand conspiracy. The truth is much less exciting: Women and men have different preferences, generally speaking.
Women are more interested in people (i.e. healthcare). Men are more interested in things (i.e. engineering).
Most nurses are women. That's not because women are activity trying to keep men out. It's because fewer men are interested or apply!
We also don’t see women in the most dangerous jobs. No one seems to have a problem with that, just as no one has a problem with most healthcare jobs being dominated by women. As they shouldn't, because people should be allowed to pursue and apply to what they want to.
Achieving that level of a standard is a balance.
There shouldn't be excuses being made. All that can do is contribute to the perpetuation of the conditions that presently exist, because the core issue isn't being identified.
Furthermore, if the core issue is the excuse itself, then again, this is covered under the umbrella of 'if people would just let them'. The secondary issue would then be that the core issue isn't being questioned.
I want to consider your, er, argument, in best possible faith, but you've given me almost nothing to work with here.
Sweden tried what?
Failure means what?
I'm hoping industries that hire young are seeing different numbers than I did, because that should signal a shift in older ones that hire senior discipline engineers after a decade or so.
Edit: that said, companies should continue to do what they can to remediate this, but I am furious that the government has done almost nothing about the issue. The underrepresented remain exactly that.
The painful stuff is when it's obvious and provable, because it highlights all the times it can be questionable as to whether it occurs.
In my experience in the industry, this is a laughable statement to make. It's a shame that unless one is a victim of unconscious, systemic bias, one is so much less likely to acknowledge it as a problem, that actually exists, and hurts people all the time.
If that's your point, I guess my counter argument is myself, not a victim, very aware of the problem, and acknowledging it as a problem as my post.
There's also the victims of conscious bias that would probably be able to acknowledge the problem...
Am I misreading your post?
It doesn't matter if it's intentional or not to a victim. It's still the same system, same cause and effect, same yield of powerlessness.
Whichever way you want to see it.
+ If it's intentional it's not unconscious.
+ If it's unconscious it's part of a culture that tolerates the behavior to the point that it doesn't get questioned.
+ If it does get questioned, eventually people are just playing dumb or it becomes intentional - if it's provable that it continues to occur.
It was still just a trickle, for the same reason you stated - very very few woman apply to tech positions.
Time and time again people (mostly men of course) keep asking "but why? why aren't there more women in the field?" Time and time again they keep saying "but I don't see any sexism in the workplace, it's nothing like it used to be, it's practically a meritocracy these days!" Yes, indeed, it truly is a giant mystery.
And yet, at the same time there is a constant deluge of stories about rampant sexism in the industry. Of all sorts, at all levels, at almost every company, and often of shockingly regressive character even up through the present time. There are countless stories in the industry of how women in tech are persistently denigrated, how men talk over them in meetings, how their ideas are ignored until they come out of the mouth of a man, how sexual harassment is ubiquitous, how they are routinely excluded from workplace culture through extremely male-centric activities that include things as ridiculous as morale events or even meetings held at strip clubs.
All of this takes a toll, and that toll is ultimately to stunt the careers of women in tech and to push women out of the industry entirely. Working in tech as a woman is climbing a hill with a much steeper slope than it is for guys. Women routinely get passed over for promotions, are routinely underpaid, routinely do not receive credit for their ideas, and routinely experience more hostile working conditions (through bias as well as sexual harassment). So they leave. They find something better to do with their time because they just can't take the stress and harassment anymore or because it just does not provide the same return on investment as it does for guys.
And we know this. We know this from studies and exposes and a torrent of anecdotes from individual women who have been in the field for years or decades. Some people (guys) have a tendency to write off each and every one of these stories and studies as somehow individual aberrations or outliers which don't have any bearing on the fundamental overall character of the industry, but this is a mistake, they are absolutely representative. The problem of over-representation of white men in tech cannot be solved by "fixing the pipeline" in the educational system nor can it be solved by making hiring processes perfectly unbiased (or even biased towards women) because the real problem is much bigger, it's systemic, widespread misogyny throughout the entire industry. That will take a tremendous amount of work to fix, but once the industry stops treating women as second class citizens (or exotic outsiders) and stops pushing them out of the industry through its toxicity then the problem will mostly fix itself.
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.
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...
This one is a bit weird, computer guys were always "nerds" and "geeks" to stay away from.
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...
We have plenty of heroes to pick from :)
>"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.
Helping individuals to overcome biology is much simpler than doing it at population scale.
Where does it state the number of applicants, male vs female?
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)
When right wing trolls attacked a female CS lecturer, she wrote a long response here: https://www.vox.com/the-big-idea/2017/8/11/16130452/google-m...
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...
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!"
* One says "executed concentration camp prisoners in Kosovo". (Yeah, ok, I'm kidding. How about "Executed a plan to reduce production costs by 30%"?)
* The other says, "Won the Women's World Chess Championship 3 years in a row".
The first has five stars (thanks to "executed") and the second has three (courtesy of "women's"). Which are you more interested in?
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?
This should be obvious when testing. Whether the algorithm discriminates should be a top priority for designing these algorithms. That's half the damn math of machine learning. If you can construct an AI, you should know how to test it for flaw in reasoning. It's just another layer of ML to do that. Outliers. It's short sighted to push these things out assuming their output is correct just because it looks 'normal'.