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this post was submitted on 12 Jul 2023
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The entire paper is already sub-field (AI) in industry (software engineering) specific. No stats are perfect, but I think these ones are pretty damn good for something where peoples role are pretty poorly determined in the first place. Of course you're welcome to try and find better ones.
The "pure tech" companies I've worked at have been roughly equivalent or better than these stats, but at that point I'm sampling from software engineers in general (not having worked at an AI specific company), and my sample is unlikely to be unbiased anyways.
AI is in all industries, from pharma, insurance, finance. Nowadays it is not even a sub field of software engineering, more of a subfield of data science. If you check the background of those who work in AI, you find the most varied combinations, from maths, to engineering, stats, and physics.
I don't have better statistics unfortunately. And I don't even want to be right.
My anecdotal experience is that women cluster in some industries, in other industries they are difficult to find, in AI more than in other subfields of data science such as what is historically defined as "statistical inference".
Again this is anecdotal, not hard science. But, as we don't have stats, better than nothing.
Edit. Again, not trying to defend anyone, just adding information for people to draw their own conclusions.
Mine are that elon was trying to save some money, and he doesn't value diversity to invest on it, and put the extra effort to create it