Messi Ho Jun Lee

Messi H. J. Lee, Soyeon Jeon

Vision-Language Models (VLMs) extend Large Language Models' capabilities by integrating image processing, but concerns persist about their potential to reproduce and amplify human biases. While research has documented how these models perpetuate stereotypes across demographic groups, most work has focused on between-group biases rather than within-group differences. This study investigates homogeneity bias-the tendency to portray groups as more uniform than they are-within Black Americans, examining how perceived racial phenotypicality influences VLMs' outputs. Using computer-generated images that systematically vary in phenotypicality, we prompted VLMs to generate stories about these individuals and measured text similarity to assess content homogeneity. Our findings reveal three key patterns: First, VLMs generate significantly more homogeneous stories about Black individuals with higher phenotypicality compared to those with lower phenotypicality. Second, stories about Black women consistently display greater homogeneity than those about Black men across all models tested. Third, in two of three VLMs, this homogeneity bias is primarily driven by a pronounced interaction where phenotypicality strongly influences content variation for Black women but has minimal impact for Black men. These results demonstrate how intersectionality shapes AI-generated representations and highlight the persistence of stereotyping that mirror documented biases in human perception, where increased racial phenotypicality leads to greater stereotyping and less individualized representation.

Messi H. J. Lee, Jacob M. Montgomery, Calvin K. Lai

Vision Language Models (VLMs), exemplified by GPT-4V, adeptly integrate text and vision modalities. This integration enhances Large Language Models' ability to mimic human perception, allowing them to process image inputs. Despite VLMs' advanced capabilities, however, there is a concern that VLMs inherit biases of both modalities in ways that make biases more pervasive and difficult to mitigate. Our study explores how VLMs perpetuate homogeneity bias and trait associations with regards to race and gender. When prompted to write stories based on images of human faces, GPT-4V describes subordinate racial and gender groups with greater homogeneity than dominant groups and relies on distinct, yet generally positive, stereotypes. Importantly, VLM stereotyping is driven by visual cues rather than group membership alone such that faces that are rated as more prototypically Black and feminine are subject to greater stereotyping. These findings suggest that VLMs may associate subtle visual cues related to racial and gender groups with stereotypes in ways that could be challenging to mitigate. We explore the underlying reasons behind this behavior and discuss its implications and emphasize the importance of addressing these biases as VLMs come to mirror human perception.

Messi H. J. Lee, Jacob M. Montgomery, Calvin K. Lai

Large language models (LLMs) are becoming pervasive in everyday life, yet their propensity to reproduce biases inherited from training data remains a pressing concern. Prior investigations into bias in LLMs have focused on the association of social groups with stereotypical attributes. However, this is only one form of human bias such systems may reproduce. We investigate a new form of bias in LLMs that resembles a social psychological phenomenon where socially subordinate groups are perceived as more homogeneous than socially dominant groups. We had ChatGPT, a state-of-the-art LLM, generate texts about intersectional group identities and compared those texts on measures of homogeneity. We consistently found that ChatGPT portrayed African, Asian, and Hispanic Americans as more homogeneous than White Americans, indicating that the model described racial minority groups with a narrower range of human experience. ChatGPT also portrayed women as more homogeneous than men, but these differences were small. Finally, we found that the effect of gender differed across racial/ethnic groups such that the effect of gender was consistent within African and Hispanic Americans but not within Asian and White Americans. We argue that the tendency of LLMs to describe groups as less diverse risks perpetuating stereotypes and discriminatory behavior.