• AI Fairness
• Demographic Bias Evaluation
• Generative AI
• Text-to-Image Models

AI-generated images can inherit and amplify biases from their training data,
leading to skewed representations of gender and ethnicity. As Text-To-Image (TTI)
modelsbecomemorewidelyadopted, itiscrucialtoidentifyandaddressthesebiases
to ensure equitable and fair representations in digital media.
This thesis investigates gender and ethnicity biases in TTI models by analysing
their representation of different occupations. A set of popular occupations was
selected based on their frequency in large text corpora. For each occupation, a
large set of images was generated using Stable Diffusion [1] and FLUX [2] models
and automatically classified through an available unbiased method to extract demo-
graphic attributes such as gender and ethnicity. A comprehensive framework was
developed to manage the entire process, from image generation to validation and
classification. It computes bias metrics to assess the over and underrepresentation
of demographic groups in generated data, ultimately enabling interactive visualisa-
tion for deeper analysis. Furthermore, the framework facilitates comparisons with
real-world statistics, enabling an assessment of the model’s bias with respect to the
actual demographic distributions. Although the framework is designed to operate
with real-world data, we showcase its reliability and effectiveness using synthetically
generated data simulating “real-world” statistics.
A series of experiments conducted using the proposed framework demonstrates
that state-of-the-art TTI models exhibit a stereotyped and biased representation of
the world, and allows for the automatic assessment of these aspects in additional
models with respect to specific populations.