"Some sexes" is a weird way of saying that, and no. However, women experimentally have a narrower IQ distribution, which means that low and high IQs tend to be dominated by men. It's (I suspect) why there are more male nobel laureates (and programmers) but also more male prisoners.
This is also the expected result if you're familiar with GMV as mediated by sex chromosome pseudodominance in most mammals, including humans.
"few sex differences (if any) remain statistically significant"
It amuses me that any article about racism and sexism in the tech inevitably devolves to "It's not racism and sexism if we just think that other races and sexes don't perform well in the tech industry."
I’m not the OP, but I doubt anyones saying that. If anything you’re asking for a long drawn out fight over why some sexes don’t major in STEM more.
The argument is going to boil down to the “pipeline” problem, ‘there’s not enough to begin with for it to be represented in proportion’, which leads to the moral hazard dilemma of do we just started filling quotas.
The OP has clarified that he thinks that women inherently do not have the skills needed for the tech industry. It's not an unusual attitude, and challenges your assessment that this is a 'pipeline' problem.
As long as tech workers think that some races have 'cultural' problems and as long as some tech workings think that there are differences between men's and women's brains that make women less suited for tech work, I think we have to stop dismissing this as a pipeline problem.
The prejudice is real and all over this thread posters are happily justifying it.
You should try to learn how to discern the difference between “<group> doesn’t have the skills for <activity>” and “<group> is statistically less likely to match selective criteria of <activity>”. It’s a pretty critical distinction.
They didn't say "they all look alike", they said that white faces have more contrast so are easier to differentiate even with a naïve CV algorithm. It's one more source of systemic bias in the ML literature, especially given the comparative scarcity of source data from non-majority groups.
> It's one more source of systemic bias in the ML literature, especially given the comparative scarcity of source data from non-majority groups.
centimeter's posts in that thread directly reject your proffered line of thought:
> your training data does not have enough people with dark skin or African American face features
This isn’t the issue - the issue is lower variance across black faces in any basis.
Here's what I said: "if you partitioned the faces by race the output of the SVD would be much wider for white people [...] white people have more light/dark contrast, more hair colors, more eye colors, etc.". This is an obvious fact that is widely recognized by CV practitioners.
I'm not sure I understand the distinction. If there are differences in 'population-level' intelligence, then some populations are less intelligent, correct?
If some populations are less intelligent, then it is OK to not hire them in the tech industry.
In your opinion, how do you tell the less intelligent populations from the more intelligent populations?
You can't use statements about populations to draw inferences about individuals - and we hire individuals, not populations. Your entire framing of this issue is 100% backwards.