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Gender bias in open source: Pull request acceptance of women versus men (www.researchgate.net)

submitted 5 months ago by [email protected] to c/[email protected]

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Our results show that women's contributions tend to be accepted more often than men's [when their gender is hidden]. However, when a woman's gender is identifiable, they are rejected more often. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

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[–] [email protected] 8 points 5 months ago* (last edited 5 months ago) (1 children)

Thanks for pointing that out.

Seems like a wild idea as... a) it poisons the data not only for AI but also real users like me (I swear I'm not a bot :D). b) if this approach is used more widely, AIs will learn very fast to identify and ignore such non-sense links and probably much faster than real humans.

It sounds like a similar concept as captchas which annoy real people, yet fail to block out bots.

[–] [email protected] 3 points 5 months ago

Yeah, that is my take as well, at first I thought it was completely useless just like the old Facebook posts with users posting a legaliese sounding text on their profile trying to reclaim rights that they signed away when joining facebook, but here it is possible that they are running their own instance so there is no unified EULA, which gives the license thing a bit more credibillity.

But as you say, bots will just ignore the links, and no single person would stand a chance against big AI with their legal teams, and even if they won the AI would still have been trained on their data, and they would get a pittance at most.