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Attenuating Bias in Word Vectors
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Word vector representations are well developed tools for various NLP and Machine Learning tasks and are known to retain significant semantic and syntactic structure of languages. But they are prone to carrying and amplifying bias which can perpetrate discrimination in various applications. In this work, we explore new simple ways to detect the most stereotypically gendered words in an embedding and remove the bias from them. We verify how names are masked carriers of gender bias and then use that as a tool to attenuate bias in embeddings. Further, we extend this property of names to show how names can be used to detect other types of bias in the embeddings such as bias based on race, ethnicity, and age.
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Mitigating Gender Bias in Contextual Word Embeddings
Regularized masked-language modeling and name-masking reduce gender bias in embeddings, but the contextual results rely heavily on evaluation metrics aligned with the training objective.
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