mlsanityData to 7 Oct 2026

Conceptor Debiasing of Word Representations Evaluated on WEAT

arXiv 1906.05993 ↗First posted 14 Jun 2019cs.CLcited by 18

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Abstract (arXiv)

Bias in word embeddings such as Word2Vec has been widely investigated, and many efforts made to remove such bias. We show how to use conceptors debiasing to post-process both traditional and contextualized word embeddings. Our conceptor debiasing can simultaneously remove racial and gender biases and, unlike standard debiasing methods, can make effect use of heterogeneous lists of biased words. We show that conceptor debiasing diminishes racial and gender bias of word representations as measured using the Word Embedding Association Test (WEAT) of Caliskan et al. (2017).

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jun 2019.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

18 papers.

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  1. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  2. 2021Debiasing Pre-trained Contextualised Embeddingscited by 107
  3. 2021Sustainable Modular Debiasing of Language Modelscited by 98
  4. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  5. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
  6. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  7. 2021Unmasking the Mask -- Evaluating Social Biases in Masked Language Modelscited by 60
  8. 2022Choose Your Lenses: Flaws in Gender Bias Evaluationcited by 29
  9. 2023Sociodemographic Bias in Language Models: A Survey and Forward Pathcited by 27
  10. 2021Learning to Remove: Towards Isotropic Pre-trained BERT Embeddingcited by 23
  11. 2022Fair NLP Models with Differentially Private Text Encoderscited by 9
  12. 2021Evaluating Metrics for Bias in Word Embeddingscited by 7
6 more
  1. 2022Bridging Fairness and Environmental Sustainability in Natural Language Processingcited by 5
  2. 2022Conceptor-Aided Debiasing of Large Language Modelscited by 4
  3. 2024Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Wayscited by 4
  4. 2026Conceptors for Semantic Steeringcited by 1
  5. 2022The SAME score: Improved cosine based bias score for word embeddingscited by 0
  6. 2022To Know by the Company Words Keep and What Else Lies in the Vicinitycited by 0