Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender

• URL: http://arxiv.org/abs/2310.19130v2
• Date: Mon, 20 Nov 2023 12:15:19 GMT
• Title: Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
• Authors: Ahmed Sabir, Llu\'is Padr\'o
• Abstract summary: We investigate the impact of objects on gender bias in image captioning systems. 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.
• Score: 1.4322753787990035
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Code and data are publicly available at \url{https://github.com/ahmedssabir/GenderScore}.

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