Assessing gender–object association bias in text-to-image generation

Text-to-image (T2I) generative AI models enable scalable visual content creation but often embed or even amplify societal biases, particularly around gender and object associations. For example, T2I models may reinforce restrictive stereotypes by depicting objects like flowers as feminine or laptops as masculine. Current bias assessment methods often involve multi-step evaluation procedures and typically emphasize aggregate demographic distributions, prompt adherence, or profession-level associations rather than object-level gender–object association analysis. This paper proposes a bias assessment pipeline that combines object detection, perceived-gender identification, object-association scoring, and heatmap visualization to analyse gender–object association bias in T2I-generated images. To support evaluation, we introduce the Object Bias Image Dataset (OBID), designed with a 10,000-image generation target, and evaluate the proposed pipeline on 3,247 retained images that satisfy the requirements for gender–object association analysis. Results reveal stronger class-wise diagonal agreement for neutral objects (0.59) than for male-associated (0.27) and female-associated (0.21) objects, highlighting both the feasibility and current limitations of automated object-association bias assessment.