The account eventually gains some level of recognition - while you have vocal haters, you also have people that upvote their content (likely based on the username) to the frontpage. Most of what gallowboob posts is garbage and I guarantee you that if it was coming from accounts with less karma or notariety it wouldn't reach the front-page. You have to consider that some of the people upvoting are people commenting on the threads.
It's hard to say which layer you are working with without having to know the whole model. You can always look at data as initial to what is computed, but 1. That's not much different than looking at the code and 2. Everything that is initial probably can be defined in terms of what computed it, unless you want to draw some arbitrary wiggly line between perception and act of taking a record or something like that, in which there are tons of use cases where one defines the other, no matter what sort of nomenclature you start with.
> I suppose if one holds the view that men and women are mentally identical on average, it would lead one to the conclusion that the gender disparity in diagnosis rates is due to social factors (or some kind of medical discrimination).
It might be easier to think about this on a different level of granularity. The problem has a self referential issue (requires social to evaluate social) if discrimination is the largest factor considered. Aggregate social factors over time yield discrimination - can't be corrected if the solution defines the problem.
If math is invented, math is the union of thought, in relation to an objective measurement of reality, because the language expresses consistent and testable truths that can be observed and measured.
If math is discovered, math is the reality, that is tested against itself.
As for thought patterns and special ways of thinking - there's nothing wrong with nurturing an interest.
I think any thought patterns that exaggerate or minimize one's ability in math simply get in the way of doing math, eventually. Pure mathematics is done for itself. Applied mathematics is done for something else.
These things are so simple when they can cleanly be separated in descriptive rhetoric, but in the actual domains of math, they could not be more complicated.
Mathematics is invented - it is only an approximation to reality. The "truths" of mathematics are what you start with - I am finding that looking at the Metamath system is a good way to see this.
It is a very useful tool in many areas but is just plain useless in others. Unfortunately, in my little opinion, there are too many people who give too much credence to the use of mathematics to discover the "truths" of the universe. They have an idealised perspective as to its efficacy instead of having a more pragmatic perspective.
I don't know if you can concretely say that mathematics is invented. Where does it come from? You observe reality, but what if reality is math?
You can't say it exists outside of the realm of possibility that some people have a base observation that rests more on math than it does anything else.
Your transition into pragmaticism is appropriate for your argument, but I don't know if it's appropriate for mathematics that concerns computation. The universe in the argument I am constructing would be the universe of math, not the physical universe. In that regard, I think pragmaticism can still be held as a primary value that can be realistically met.
Where does mathematics come from? Good question. How about a not so satisfactory answer - we like to make sense of the world around us and we also have the ability to map the world around into patterns.
With that, we try to put what we observe into some possibly useful form. Now those attempts at making patterns can take a number of different forms, of which one is mathematics.
In the case of mathematics, we create axioms as a basic framework on which we build the various mathematical edifices that we use. We have applicable rules that are used and from that people come up with all the various fields in mathematics that exist today.
However, as part of a challenge that I accepted some time ago, I am finding that everything we use in mathematics is but a map of reality and not reality itself. No matter what mathematics one uses in whatever area one uses mathematics, it is only an approximation to the reality around us. When the rubber hits the road, our mathematics often fails us. Forty years ago, one of my engineering lecturers advised all of us students at the time that though mathematics could give precise answers, reality had a penchant for not being precise at all, as in, there is much variation in the physical mediums in which we would work. Always treat all mathematical results obtained as only being approximate and design for the worst case scenario.
Every map in existence is but an inaccurate description of the observable universe. From the very small to the very large, from the very simple to the very complex, mathematics gives us a possible way of inadequately describing what we see. Though, in its inadequacy, mathematics provides a means of studying and manipulating the universe around us from which we can get useful results.
So we have all sorts of interesting technology and science with which we manipulate our environments.
The problem I see is that there are those who attribute an unwarranted "honour" (I suppose) to mathematics without stopping and seeing its limitations and that it is a tool which we have created or invented so that we can make sense of the universe around us. Studying the Metamath system is interesting for me in that by using some very simple rules we can build all the mathematics of today.
What I find interesting in your last paragraph is that you, yourself, seem to make the distinction between reality and mathematics, which would indicate, at least to me, that you recognise that it is but a tool created for a use and not something that had a pre-existence that we could discover. However, I may be completely misunderstanding your argument at this point.
>Every map in existence is but an inaccurate description of the observable universe.
That's the thing though: mathematics can be inspired by the world around us, but more often than not, it does not aim to describe it.
Mathematicians working, say, on exotic 4-manifolds, are not concerned in the least with how well these structure describe anything out there in the "real world" of yours. The short answer is: it doesn't. The longer answer is: 4-manifolds are the real world, or, rather a part of it that, perhaps, the only way you can observe and interact with that part of the real world is through mathematics.
> What I find interesting in your last paragraph is that you, yourself, seem to make the distinction between reality and mathematics, which would indicate, at least to me, that you recognise that it is but a tool created for a use and not something that had a pre-existence that we could discover.
No, I continue to express uncertainty and doubt concerning the origin of math. If math is the ground for some people, math does not have an origin. Math is the origin.
I understand that may be a very difficult paradigm to think with if you are not used to thinking in that framework, but I am like that. Math, ground. Reality is defined by math. We may be incorrect in the translation, but that's a work in progress. Math that comes from reality is math that is incomplete.
I can't give you a question that yields a satisfactory answer until it does.
I hate this industry. Shooting themselves in the foot over and over again because no one can get passed the idea that possibly, women can be just as good at math, logic and computer science - if people would just let them. This never ends. It's just one place after another, when it gets discovered. It never changes.
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.
There's no actual evidence to suggest preference for career type is inherent to being a woman or man. There's plenty showing that women right now have different interests of men, but the cause can easily be societal conditioning - i.e., something that can be fixed.
That probably does play a part but it's a different level than what we're talking about. Its more narrowly about whether people's current interests are being allowed.
I think you need to read the comment again. The author's reply was to your comment saying "if people would just let them", not what the ML algorithm does in the article.
That's somewhat appropriate, but, I think having higher standards for identifying discriminatory practices is covered under the umbrella of 'if people would just let them'.
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 understand your frustration, but in my experience recruiting, the primary reason behind there being less women getting hired into engineering roles is almost never raw sexism. Maybe in the 90s, but in the early 10s there was tons of policy around it, bosses were setting the culture, we were doing everything "right." But we were still not hiring that many women, simply because hardly any women ever applied. For chem e, Mech e, EE roles with 100 applicants, usually I'd see at most one female applicant. It was rare to getm but when we did we'd push for the interview and they'd get through with an average success rate (compared to make applicants).
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.
Yes, I am female, I am aware of the statistics. It's frustrating because my life literally gets impacted by automated reasoning such as this. It's not frustrating for anyone who says 'there just aren't enough of you'. That's something that is very easy to say by people who never have to experience that sort of discriminatory practice.
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.
I worked in a tech company which was heavily biased in favor of hiring woman, there were special hiring tracks for woman where the interviews were easier and the interviewers received special training in unconscious biases, and the managers received bonuses for having a closer to 50/50 ratio.
It was still just a trickle, for the same reason you stated - very very few woman apply to tech positions.
> but in my experience recruiting, the primary reason behind there being less women getting hired into engineering roles is almost never raw sexism.
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.
I don't believe in unconscious thought, but if unconscious thought were real, it would be obvious that the only people who can be aware of it's effects are the people who can identify a difference between those two states of being, and be able to reduce that down to an model/abstraction/statement.
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.
I find this sort of sentiment almost hilarious in how out of touch it is.
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.
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.
Your comment will probably end up buried, but it does raise the question - if they want more female employees, was the issue in the training data, or their recruitment process?
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'.
