Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models
Journal:
arXiv
Published Date:
May 22, 2025
Abstract
The biases exhibited by text-to-image (TTI) models are often treated as
independent, though in reality, they may be deeply interrelated. Addressing
bias along one dimension - such as ethnicity or age - can inadvertently affect
another, 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. To address this, we introduce BiasConnect, a novel tool for
analyzing and quantifying bias interactions in TTI models. BiasConnect uses
counterfactual interventions along different bias axes to reveal the underlying
structure of these interactions and estimates the effect of mitigating one bias
axis on another. These estimates show strong correlation (+0.65) with observed
post-mitigation outcomes. Building on BiasConnect, we propose InterMit, an
intersectional bias mitigation algorithm guided by user-defined target
distributions and priority weights. InterMit achieves lower bias (0.33 vs.
0.52) with fewer mitigation steps (2.38 vs. 3.15 average steps), and yields
superior image quality compared to traditional techniques. Although our
implementation is training-free, InterMit is modular and can be integrated with
many existing debiasing approaches for TTI models, making it a flexible and
extensible solution.
