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    <titleInfo>
        <title>Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender</title>
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            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
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            <namePart type="given">Juan</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
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    <abstract>In this paper, we investigate the impact of objects on gender bias in image captioning systems. Our results show that only gender-specific objects have a strong gender bias (e.g., women-lipstick). In addition, we propose a visual semantic-based gender score that measures the degree of bias and can be used as a plug-in for any image captioning system. Our experiments demonstrate the utility of the gender score, since we observe that our score can measure the bias relation between a caption and its related gender; therefore, our score can be used as an additional metric to the existing Object Gender Co-Occ approach.</abstract>
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%0 Conference Proceedings
%T Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
%A Sabir, Ahmed
%A Padró, Lluís
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F sabir-padro-2023-women
%X In this paper, we investigate the impact of objects on gender bias in image captioning systems. Our results show that only gender-specific objects have a strong gender bias (e.g., women-lipstick). In addition, we propose a visual semantic-based gender score that measures the degree of bias and can be used as a plug-in for any image captioning system. Our experiments demonstrate the utility of the gender score, since we observe that our score can measure the bias relation between a caption and its related gender; therefore, our score can be used as an additional metric to the existing Object Gender Co-Occ approach.
%U https://aclanthology.org/2023.findings-emnlp.279
%P 4234-4240

Markdown (Informal)

[Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender](https://aclanthology.org/2023.findings-emnlp.279) (Sabir & Padró, Findings 2023)

• Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender (Sabir & Padró, Findings 2023)

ACL

• Ahmed Sabir and Lluís Padró. 2023. Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4234–4240, Singapore. Association for Computational Linguistics.