2023
Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
EMNLP 2023short findings
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 b…