# Conceptor Debiasing of Word Representations Evaluated on WEAT

arXiv: https://arxiv.org/abs/1906.05993 · HTML: https://mlsanity.com/paper/1906.05993/

## Key facts

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

## Built on by (most cited first, 18 of 18)

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- [Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways](https://mlsanity.com/paper/2402.04420/) (2024)
- [Conceptors for Semantic Steering](https://mlsanity.com/paper/2605.04980/) (2026)
- [The SAME score: Improved cosine based bias score for word embeddings](https://mlsanity.com/paper/2203.14603/) (2022)
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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).