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What do you mean by pretty serious? I looked at three, two are about better documenting ML models/data, and one is about inferring the political leanings of an area by what type of cars are parked in it. None seem particularly groundbreaking to me...

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Honestly? I don't have much experience with ML and in particular issues of bias in ML (though I have a general understanding of a lot of the former), so I don't really feel qualified to evaluate her more recent work, but her older stuff definitely appeared like what I'd expect from a machine learning practitioner, so I don't think attacking her as someone that only understands social sciences (which I perceived the parent as doing) to be fair.

Her work on identifying bias in facial recognition has won quite a number of awards.

Ground-breaking might be a strong word but it does move the industry forward.

Do you have a link to that paper? I'd like to read it, though I wasn't thrilled by the way she handled (or didn't) Yann LeCun.

You can see some of the recognition on her Wikipedia page:

https://en.wikipedia.org/wiki/Timnit_Gebru

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