Nice stats, but it isn’t broken down on industry. From experience (I worked in different fields) in some industries such as pharma, people analytics or marketing, women are even likely the majority (they were majority when I worked in pharma, for instance). In more “pure” tech and fintech companies, I do not believe those stats represent the “natural distribution”. I know it’s anecdotal, but trust me, it’s not easy to find woman in AI in some industries. They are highly valued, well paid and have quick career progression because of this, to attract and retain them.
That said, elon is probably “machist” type of guy, I am not defending him. Just trying to give a bit of context
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
Saying “coincidence” is basically claiming there is no reason for an observed pattern. This is really more of a last resort when considering explanations for certain patterns, because it’s probably the weakest claim someone can make.
Generally, patterns are not coincidence because if an outcome is truly a result of randomness, then there is an extremely low chance that there would be a pattern.
Also, 12 is not the whole data set. The whole data set should include the people who weren’t hired during the hiring process. This is unknown to us.
Taking 89.3% men from your source at face value, and selecting 12 people at random, that gives a 12.2% chance (1 in 8) that the company of that size would be all male.
Add in network effects, risk tolerance for startups, and the hiring practices of larger companies, and that number likely gets even larger.
What’s the p-value for a news story? Unless this is some trend from other companies run by Musk, there doesn’t seem to be anything newsworthy here.
Eh, the gender imbalance is bad, but not 0/12 bad… here are some stats
Those stats don’t take into account the number of women that would want to work for Elon Musk.
Isn’t the fact that he’s repulsive sort of the whole complaint?
Just saying… if you take that variable into account it probably gets a lot closer to that 0/12
Nice stats, but it isn’t broken down on industry. From experience (I worked in different fields) in some industries such as pharma, people analytics or marketing, women are even likely the majority (they were majority when I worked in pharma, for instance). In more “pure” tech and fintech companies, I do not believe those stats represent the “natural distribution”. I know it’s anecdotal, but trust me, it’s not easy to find woman in AI in some industries. They are highly valued, well paid and have quick career progression because of this, to attract and retain them.
That said, elon is probably “machist” type of guy, I am not defending him. Just trying to give a bit of context
“my anecdotal industry experience trumps your stats” you don’t sound like you have a very unbiased opinion brah
If you read again, you’ll see I am saying that the stat is not complete as it doesn’t drill down to industries brah.
In absence of statistics anecdotal evidence is better than nothing to draw qualitative conclusions brah
If you have different experience, I am happy to discuss
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
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Saying “coincidence” is basically claiming there is no reason for an observed pattern. This is really more of a last resort when considering explanations for certain patterns, because it’s probably the weakest claim someone can make.
Generally, patterns are not coincidence because if an outcome is truly a result of randomness, then there is an extremely low chance that there would be a pattern.
Also, 12 is not the whole data set. The whole data set should include the people who weren’t hired during the hiring process. This is unknown to us.
deleted by creator
Taking 89.3% men from your source at face value, and selecting 12 people at random, that gives a 12.2% chance (1 in 8) that the company of that size would be all male.
Add in network effects, risk tolerance for startups, and the hiring practices of larger companies, and that number likely gets even larger.
What’s the p-value for a news story? Unless this is some trend from other companies run by Musk, there doesn’t seem to be anything newsworthy here.
deleted by creator