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Understanding the Origins of Bias in Word Embeddings
Marc-Etienne Brunet · Colleen Alkalay-Houlihan · Ashton Anderson · Richard Zemel

Wed Jun 12 04:40 PM -- 05:00 PM (PDT) @ Seaside Ballroom

Popular word embedding algorithms exhibit stereotypical biases, such as gender bias. The widespread use of these algorithms in machine learning systems can amplify stereotypes in important contexts. Although some methods have been developed to mitigate this problem, how word embedding biases arise during training is poorly understood. In this work we develop a technique to address this question. Given a word embedding, our method reveals how perturbing the training corpus would affect the resulting embedding bias. By tracing the origins of word embedding bias back to the original training documents, one can identify subsets of documents whose removal would most reduce bias. We demonstrate our methodology on Wikipedia and New York Times corpora, and find it to be very accurate.

Author Information

Marc-Etienne Brunet (University of Toronto)
Colleen Alkalay-Houlihan (University of Toronto)
Ashton Anderson (University of Toronto)
Richard Zemel (Vector Institute)

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