Visual Cues of Gender and Race are Associated with Stereotyping in Vision-Language Models

Current research on bias in Vision Language Models (VLMs) has important limitations: it is focused exclusively on trait associations while ignoring other forms of stereotyping, it examines specific contexts where biases are expected to appear, and it conceptualizes social categories like race and gender as binary, ignoring the multifaceted nature of these identities. Using standardized facial images that vary in prototypicality, we test four VLMs for both trait associations and homogeneity bias in open-ended contexts. We find that VLMs consistently generate more uniform stories for women compared to men, with people who are more gender prototypical in appearance being represented more uniformly. By contrast, VLMs represent White Americans more uniformly than Black Americans. Unlike with gender prototypicality, race prototypicality was not related to stronger uniformity. In terms of trait associations, we find limited evidence of stereotyping-Black Americans were consistently linked with basketball across all models, while other racial associations (i.e., art, healthcare, appearance) varied by specific VLM. These findings demonstrate that VLM stereotyping manifests in ways that go beyond simple group membership, suggesting that conventional bias mitigation strategies may be insufficient to address VLM stereotyping and that homogeneity bias persists even when trait associations are less apparent in model outputs.

Homogeneity Bias as Differential Sampling Uncertainty in Language Models

Prior research show that Large Language Models (LLMs) and Vision-Language Models (VLMs) represent marginalized groups more homogeneously than dominant groups. However, the mechanisms underlying this homogeneity bias remain relatively unexplored. We propose that this bias emerges from systematic differences in the probability distributions from which tokens are sampled at inference-time. Analyzing three measures of uncertainty in token sampling distributions-entropy, perplexity, and probability of differentiation-we find that in some models, specifically GPT-4 Turbo and Llama-3.2, tokens are sampled more deterministically when generating texts about marginalized groups (i.e., Black Americans and women) compared to their dominant group counterparts (i.e., White Americans and men). While these findings may help explain homogeneity bias in certain models, the patterns did not replicate across all VLMs tested, suggesting multiple mechanisms may contribute to homogeneity bias in AI.

Vision-Language Models Represent Darker-Skinned Black Individuals as More Homogeneous than Lighter-Skinned Black Individuals

Vision-Language Models (VLMs) combine Large Language Model (LLM) capabilities with image processing, enabling tasks like image captioning and text-to-image generation. Yet concerns persist about their potential to amplify human-like biases, including skin tone bias. Skin tone bias, where darker-skinned individuals face more negative stereotyping than lighter-skinned individuals, is well-documented in the social sciences but remains under-explored in Artificial Intelligence (AI), particularly in VLMs. While well-documented in the social sciences, this bias remains under-explored in AI, particularly in VLMs. Using the GAN Face Database, we sampled computer-generated images of Black American men and women, controlling for skin tone variations while keeping other features constant. We then asked VLMs to write stories about these faces and compared the homogeneity of the generated stories. Stories generated by VLMs about darker-skinned Black individuals were more homogeneous than those about lighter-skinned individuals in three of four models, and Black women were consistently represented more homogeneously than Black men across all models. Interaction effects revealed a greater impact of skin tone on women in two VLMs, while the other two showed nonsignificant results, reflecting known stereotyping patterns. These findings underscore the propagation of biases from single-modality AI systems to multimodal models and highlight the need for further research to address intersectional biases in AI.