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    <titleInfo>
        <title>On Gender Biases in Offensive Language Classification Models</title>
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    <name type="personal">
        <namePart type="given">Sanjana</namePart>
        <namePart type="family">Marcé</namePart>
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            <title>Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)</title>
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        <name type="personal">
            <namePart type="given">Christian</namePart>
            <namePart type="family">Hardmeier</namePart>
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        <name type="personal">
            <namePart type="given">Christine</namePart>
            <namePart type="family">Basta</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Marta</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Costa-jussà</namePart>
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            <namePart type="given">Gabriel</namePart>
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        <name type="personal">
            <namePart type="given">Hila</namePart>
            <namePart type="family">Gonen</namePart>
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    <abstract>We explore whether neural Natural Language Processing models trained to identify offensive language in tweets contain gender biases. We add historically gendered and gender ambiguous American names to an existing offensive language evaluation set to determine whether models? predictions are sensitive or robust to gendered names. While we see some evidence that these models might be prone to biased stereotypes that men use more offensive language than women, our results indicate that these models? binary predictions might not greatly change based upon gendered names.</abstract>
    <identifier type="citekey">marce-poliak-2022-gender</identifier>
    <identifier type="doi">10.18653/v1/2022.gebnlp-1.19</identifier>
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        <url>https://aclanthology.org/2022.gebnlp-1.19</url>
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        <date>2022-07</date>
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            <start>174</start>
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%0 Conference Proceedings
%T On Gender Biases in Offensive Language Classification Models
%A Marcé, Sanjana
%A Poliak, Adam
%Y Hardmeier, Christian
%Y Basta, Christine
%Y Costa-jussà, Marta R.
%Y Stanovsky, Gabriel
%Y Gonen, Hila
%S Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F marce-poliak-2022-gender
%X We explore whether neural Natural Language Processing models trained to identify offensive language in tweets contain gender biases. We add historically gendered and gender ambiguous American names to an existing offensive language evaluation set to determine whether models? predictions are sensitive or robust to gendered names. While we see some evidence that these models might be prone to biased stereotypes that men use more offensive language than women, our results indicate that these models? binary predictions might not greatly change based upon gendered names.
%R 10.18653/v1/2022.gebnlp-1.19
%U https://aclanthology.org/2022.gebnlp-1.19
%U https://doi.org/10.18653/v1/2022.gebnlp-1.19
%P 174-183

Markdown (Informal)

[On Gender Biases in Offensive Language Classification Models](https://aclanthology.org/2022.gebnlp-1.19) (Marcé & Poliak, GeBNLP 2022)

• On Gender Biases in Offensive Language Classification Models (Marcé & Poliak, GeBNLP 2022)

ACL

• Sanjana Marcé and Adam Poliak. 2022. On Gender Biases in Offensive Language Classification Models. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 174–183, Seattle, Washington. Association for Computational Linguistics.