AI-generated faces free from racial and gender stereotypes
Nouar AlDahoul Talal Rahwan Yasir Zaki
Text-to-image generative AI models such as Stable Diffusion are used daily by
millions worldwide. However, many have raised concerns regarding how these
models amplify racial and gender stereotypes. To study this phenomenon, we
develop a classifier to predict the race, gender, and age group of any given
face image, and show that it achieves state-of-the-art performance. Using this
classifier, we quantify biases in Stable Diffusion across six races, two
genders, five age groups, 32 professions, and eight attributes. We then propose
novel debiasing solutions that outperform state-of-the-art alternatives.
Additionally, we examine the degree to which Stable Diffusion depicts
individuals of the same race as being similar to one another. This analysis
reveals a high degree of stereotyping, e.g., depicting most middle eastern
males as being dark-skinned, bearded, and wearing a traditional headdress. We
address these limitations by proposing yet another novel solution that
increases facial diversity across genders and racial groups. Our solutions are
open-sourced and made publicly available.