Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
Ahmed Sabir, Lluís Padró · 2023
In this paper, we investigate the impact of objects on gender bias in image captioning systems.Our results show that only genderspecific 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 https://github.com/ ahmedssabir/GenderScore.