Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models (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.

## introduction text-to-image (tti) models such as dall-e @xcite , imagen @xcite , and stable diffusion @xcite have become widely used for generating visual content from textual prompts. despite their impressive capabilities, these models often inherit and amplify biases present in their training data @xcite @xcite . these biases manifest across multiple social and figure 1 : an example for which biasconnect estimates a negative impact of bias mitigation along one axis on another axis. for this query, increasing the gender diversity (gen) skews age distribution (age) for images of musicians generated by flux-dev. the concept of intersectionality, first introduced by crenshaw @xcite , motivates the need to understand how overlapping social identities such as race, gender, and class contribute to systemic inequalities. in tti models, these intersections can have a significant impact. as a motivating study, we independently mitigated eight bias dimensions over 26 occupational prompts on stable diffusion 1.4, using a popular bias mitigation strategy, iti-gen @xcite . we found that while the targeted biases were reduced in most cases, biases along other axes were negatively affected in over 29% of the cases. this suggests that for an effective bias mitigation strategy, it is crucial to understand which biases are intersectional. additionally, it is important to strive towards building a more holistic bias mitigation algorithm that can either mitigate multiple biases simultaneously or predict what biases cannot be mitigated together. to understand how biases in tti models influence one another, we propose biasconnect, the first analysis tool that evaluates biases while explicitly modeling their intersectional relationships. unlike prior methods that treat biases in isolation, biasconnect identifies how mitigating one bias can positively or negatively affect others. specifically, biasconnect uses a novel metric, the intersectional sensitivity (is ), to quantify how mitigation along one axis affects others. these is scores show a strong correlation (+0.65) with observed intersectional outcomes post-mitigation. we validate our approach through robustness studies and qualitative analyses, demonstrating its utility for auditing open-source tti models. furthermore, we extend biasconnect with a holistic intersectional bias mitigation algorithm, intermit. while we propose an effective and straightforward implementation in this paper, intermit is modular and can be integrated with any existing sequential bias mitigation method. unlike prior approaches that assume fixed ideal distributions and treat all biases equally, intermit allows users to define arbitrary target distributions, select specific bias axes, and assign custom priority weights to each bias-enabling flexible joint mitigation and informed reasoning about conflicting biases. in our evaluation, intermit outperforms existing methods by mitigating biases more effectively, producing higher-quality images, and requiring fewer mitigation steps. moreover, unlike other methods, it can handle a larger number (> 3) of bias axes and alerts users when mitigation along one axis adversely affects others. ## approach the objective of biasconnect is to identify and quantify the intersectional effects of intervening on one bias axis (b x ) to mitigate that bias, on any other bias axis (b y ). biasconnect works by systematically altering input prompts and analyzing the resulting distributions of generated images (see fig. 2 ). to achieve this, we leverage counterfactual prompts by modifying specific attributes (e.g., male and female) along a bias axis (e.g., gender) and examine how these interventions impact other bias dimensions (e.g., age and ethnicity). if modifying one bias axis through counterfactual intervention causes significant shifts in the distribution of attributes along another bias axis, it indicates an intersectional dependency between these axes. we first construct prompt counterfactuals and generate images using a tti model (sec. 3.1). subsequently, to identify bias-related attributes in the generated images, we use a visual question answering (vqa) model (sec. 3.2). finally, to quantify the intersectional effects, and to identify whether these effects are positive or negative, we compute the intersectional sensitivity (sec. 3.3). ## intersectional bias mitigation using biasconnect this section introduces an iterative strategy for mitigating intersectional biases, designed to be modular and compatible with existing sequential bias mitigation algorithms. we propose a subjective, user-guided mitigation framework built on top of biasconnect, called intermit. the framework allows users to select a subset of bias dimensions from a predefined set and assign mitigation priorities to each. additionally, users can specify a desired target distribution that the model should conform to, providing both flexibility and control over the mitigation process. our proposed framework (in algorithm 1) leverages a matrix s at each step to iteratively reduce bias across multiple axes. given a tti model m , a subset of selected axes by the user b * ⊆ b, let @xmath0, where |p| 1 = 1. the ideal desired distribution d * , for each bias axes in b * is also specified by the user. given the aforementioned information, we first calculate a bias score for initial model m (0) by taking the dot product of the priority vector p and the initial measures of biases b * (w init b * ) computed using eq. 3. this is denoted by τ = ⟨w init b * , p⟩ and measures the overall bias of the model on b * at any timestep. we proceed to mitigation if τ is greater than a threshold ϵ. to choose which bias axis to mitigate on, we extract the submatrix s ′ ∈ r n×|b * | consisting of the relevant columns from s obtained using biasconnect. for each row s ′ i of s ′ , we compute a similarity score γ @xmath1, p⟩, which quantifies the alignment between the i-th intersectional bias and the desired direction of mitigation. the bias axis i * = arg max i γ i with the highest alignment score is selected for targeted mitigation in the current iteration. the model is then updated to reduce bias along the direction corresponding to i * , using a mitigation method, giving m (1) . after mitigation, we generate a new set of images, recompute τ , and continue the mitigation process if τ > ϵ. ## experiments we evaluate biasconnect for its ability to study intersectional biases across multiple models and prompts (section 5.2, 5.3) and its robustness (5.4). following that, we use intermit for mitigation, and compare it to an existing strategy (5.5). ## conclusion we propose biasconnect to investigate intersectional biases in tti models. while prior research has explored bias detection and mitigation, to the best of our knowledge, no previous work has focused on understanding how biases influence one another. unlike intermit, prior bias mitigation strategies did not account for intersectional impacts. we believe our work enables a more nuanced analysis of bias interactions and supports informed decision-making for ai users and developers, fostering more equitable and transparent ai.