Ignoring the whiny feminism, this is very cool! Language use reflects reality in a deep way. https://arxiv.org/abs/1607.06520 pic.twitter.com/bdto4mKI4I
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Ignoring the whiny feminism, this is very cool! Language use reflects reality in a deep way. https://arxiv.org/abs/1607.06520 pic.twitter.com/bdto4mKI4I
@PsychRabble Some pretty cool evidence of stereotype accuracy in this paper, and some funny PC quotes you can use for upcoming papers that complain about leftism embedded in research language use. <-- Someone should show this quantitatively using machine learning!
E.g. this one is particularly obvious. Word embeddings across datasets highly correlated, and correlates pretty well with ratings by human subjects from MTurk. Still, they don't use the chance to calculate the actual accuracy using known distributions of professions! Why?pic.twitter.com/XfPJqtvbMa
This research was done down the hall from me! It's not quite right to say language use is just reflected in word embeddings. ML seeks to generalize so will amplify stereotypes.
My position is that the amplification of bias from 'reality' or our current set of training data is bad because it violates my sense of Rawlsian justice. Here is some good documentation/fixing of that amplification, also from Boston University: https://arxiv.org/abs/1803.09797
My working hypothesis is that embedding sex roles in language use acts as useful guidelines for humans and is generally speaking beneficial. Suggests well tried and proven paths for individuals to pursue based on their sex. Outliers can ignore them as they see fit.
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