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        <title>Power(ful) Associations: Rethinking “Stereotype” for NLP</title>
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            <namePart type="given">Agnieszka</namePart>
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            <namePart type="given">Christine</namePart>
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        <name type="personal">
            <namePart type="given">Marta</namePart>
            <namePart type="family">Costa-jussà</namePart>
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            <namePart type="given">Karolina</namePart>
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        <name type="personal">
            <namePart type="given">Debora</namePart>
            <namePart type="family">Nozza</namePart>
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    <abstract>The tendency for Natural Language Processing (NLP) technologies to reproduce stereotypical associations, such as associating Black people with criminality or women with care professions, is a site of major concern and, therefore, much study. Stereotyping is a powerful tool of oppression, but the social and linguistic mechanisms behind it are largely ignored in the NLP field. Thus, we fail to effectively challenge stereotypes and the power asymmetries they reinforce. This opinion paper problematizes several common aspects of current work addressing stereotyping in NLP, and offers practicable suggestions for potential forward directions.</abstract>
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%0 Conference Proceedings
%T Power(ful) Associations: Rethinking “Stereotype” for NLP
%A Devinney, Hannah
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Stańczak, Karolina
%Y Nozza, Debora
%S Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-277-0
%F devinney-2025-power
%X The tendency for Natural Language Processing (NLP) technologies to reproduce stereotypical associations, such as associating Black people with criminality or women with care professions, is a site of major concern and, therefore, much study. Stereotyping is a powerful tool of oppression, but the social and linguistic mechanisms behind it are largely ignored in the NLP field. Thus, we fail to effectively challenge stereotypes and the power asymmetries they reinforce. This opinion paper problematizes several common aspects of current work addressing stereotyping in NLP, and offers practicable suggestions for potential forward directions.
%R 10.18653/v1/2025.gebnlp-1.4
%U https://aclanthology.org/2025.gebnlp-1.4/
%U https://doi.org/10.18653/v1/2025.gebnlp-1.4
%P 52-58

Markdown (Informal)

[Power(ful) Associations: Rethinking “Stereotype” for NLP](https://aclanthology.org/2025.gebnlp-1.4/) (Devinney, GeBNLP 2025)

• Power(ful) Associations: Rethinking “Stereotype” for NLP (Devinney, GeBNLP 2025)

ACL

• Hannah Devinney. 2025. Power(ful) Associations: Rethinking “Stereotype” for NLP. In Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 52–58, Vienna, Austria. Association for Computational Linguistics.