Analysing Gender Bias in Text-to-Image Models using Object Detection
Harvey Mannering
This work presents a novel strategy to measure bias in text-to-image models.
Using paired prompts that specify gender and vaguely reference an object (e.g.
"a man/woman holding an item") we can examine whether certain objects are
associated with a certain gender. In analysing results from Stable Diffusion,
we observed that male prompts generated objects such as ties, knives, trucks,
baseball bats, and bicycles more frequently. On the other hand, female prompts
were more likely to generate objects such as handbags, umbrellas, bowls,
bottles, and cups. We hope that the method outlined here will be a useful tool
for examining bias in text-to-image models.