DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces (2021.eacl-demos)
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| Challenge: | Recent research has shown that distributional word vector spaces often encode stereotypical human biases, such as racism and sexism. |
| Approach: | They propose a platform that measures and mitigates bias in word embeddings by executing two (mutually composable) debiasing models. |
| Outcome: | The proposed platform can measure and mitiga bias in word embeddings. |
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| Challenge: | Existing methods to debias word embeddings in binary settings such as gender and religion are limited to binary labels, whereas word2vec embedders can be used to propagate biases. |
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Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation (2020.acl-main)
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| Challenge: | Existing methods to remove gender bias from word embeddings are insufficient, we argue . existing methods for gender-neutral modeling are ineffective, we conclude . |
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