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        <title>Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
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    <abstract>We present a new approach for detecting human-like social biases in word embeddings using representational similarity analysis. Specifically, we probe contextualized and non-contextualized embeddings for evidence of intersectional biases against Black women. We show that these embeddings represent Black women as simultaneously less feminine than White women, and less Black than Black men. This finding aligns with intersectionality theory, which argues that multiple identity categories (such as race or sex) layer on top of each other in order to create unique modes of discrimination that are not shared by any individual category.</abstract>
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%0 Conference Proceedings
%T Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis
%A Lepori, Michael
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F lepori-2020-unequal
%X We present a new approach for detecting human-like social biases in word embeddings using representational similarity analysis. Specifically, we probe contextualized and non-contextualized embeddings for evidence of intersectional biases against Black women. We show that these embeddings represent Black women as simultaneously less feminine than White women, and less Black than Black men. This finding aligns with intersectionality theory, which argues that multiple identity categories (such as race or sex) layer on top of each other in order to create unique modes of discrimination that are not shared by any individual category.
%R 10.18653/v1/2020.coling-main.151
%U https://aclanthology.org/2020.coling-main.151
%U https://doi.org/10.18653/v1/2020.coling-main.151
%P 1720-1728

Markdown (Informal)

[Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis](https://aclanthology.org/2020.coling-main.151) (Lepori, COLING 2020)

• Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis (Lepori, COLING 2020)

ACL

• Michael Lepori. 2020. Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1720–1728, Barcelona, Spain (Online). International Committee on Computational Linguistics.