Using Adversarial Debiasing to Remove Bias from Word Embeddings
Not on the map. Why?
Abstract (arXiv)
Word Embeddings have been shown to contain the societal biases present in the original corpora. Existing methods to deal with this problem have been shown to only remove superficial biases. The method of Adversarial Debiasing was presumed to be similarly superficial, but this is was not verified in previous works. Using the experiments that demonstrated the shallow removal in other methods, I show results that suggest Adversarial Debiasing is more effective at removing bias and thus motivate further investigation on the utility of Adversarial Debiasing.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 6 references that are in the map. First posted on arXiv 21 Jul 2021.
Builds on
6 of 6 references.
References · 6
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
- 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
- 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
- 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
- 2018Achieving Fairness through Adversarial Learning: an Application to Recidivism Predictioncited by 77
Built on by
Citing papers per year: 2021: 1; 2022: 1.
2 papers.