Our classifier finds that XX uses a more abusive language, therefore the classifier is discriminating XX. The classifier may be wrong but that's one hell of a conclusion.
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But what I really love is that they still ended up with that conclusion despite using obviously biased datasets! Except when the model was trained on the dataset annotated by "a 25 year old woman studying gender studies and a nonac- tivist feminist" to "check for bias"

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Reminds me of this 1998 Thomas Sowell quote:https://twitter.com/ThomasSowell/status/1034486520995098624?s=19 …
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It's Karens all the way down.
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It blows my mind that this stuff gets published. It took me quite a bit of time and effort to get my research published (geology) and the lack of rigor in these papers is stunning.
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Wow, gender studies has spread out into proper sciences. This study could be interesting if they looked into how various people judged the same texts.https://en.m.wikipedia.org/wiki/Ingmar_Weber …
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Why am I not surprised?
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N'oubliez pas de mentionner les gens dont vous parlez ;-)
@ingmarweber https://twitter.com/thomasrdavidson/status/1134082317629231106 …#ergonomie#hypertexte#contradictionThanks. Twitter will use this to make your timeline better. UndoUndo
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