"Researchers found that the same word-vector geometry revealed biases in the corpus, including the association of men with computer programmers and women with homemakers."
Bolukbasi and colleagues examined embeddings trained on Google News and documented stereotypical gender associations, including the computer programmer and homemaker analogy. These associations emerged from statistical language learning rather than rules explicitly assigning occupations to genders.
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