BiasConnect: Investigating Bias Interactions in Text-to-Image Models
Journal:
arXiv
Published Date:
Mar 12, 2025
Abstract
The biases exhibited by Text-to-Image (TTI) models are often treated as if
they are independent, but in reality, they may be deeply interrelated.
Addressing bias along one dimension, such as ethnicity or age, can
inadvertently influence another dimension, like gender, either mitigating or
exacerbating existing disparities. Understanding these interdependencies is
crucial for designing fairer generative models, yet measuring such effects
quantitatively remains a challenge. In this paper, we aim to address these
questions by introducing BiasConnect, a novel tool designed to analyze and
quantify bias interactions in TTI models. Our approach leverages a
counterfactual-based framework to generate pairwise causal graphs that reveals
the underlying structure of bias interactions for the given text prompt.
Additionally, our method provides empirical estimates that indicate how other
bias dimensions shift toward or away from an ideal distribution when a given
bias is modified. Our estimates have a strong correlation (+0.69) with the
interdependency observations post bias mitigation. We demonstrate the utility
of BiasConnect for selecting optimal bias mitigation axes, comparing different
TTI models on the dependencies they learn, and understanding the amplification
of intersectional societal biases in TTI models.
