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A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings

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Publication: TIPSTER (2019)

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AbstractWord embedding models have gained a lot of traction in the Natural Language Processing community, however, they suffer from unintended demographic biases. Most approaches to evaluate these biases rely on …...

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On the Generalization of Projection-Based Gender Debiasing in Word Embedding

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Publication: TIPSTER (2023)

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AbstractGender bias estimation and mitigation techniques in word embeddings lack an understanding of their generalization capabilities. In this work, we complement prior research by comparing in a systematic way four …...

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