VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models
While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less attention. Existing VLM bias studies often focus on portrait-style images and gender-occupation associations, overlooking broader and more complex social stereotypes and their implied harm. This work introduces VIGNETTE, a large-scale VQA benchmark with 30M+ images for evaluating bias in VLMs through a question-answering framework spanning four directions: factuality, perception, stereotyping, and decision making. Beyond narrowly-centered studies, we assess how VLMs interpret identities in contextualized settings, revealing how models make trait and capability assumptions and exhibit patterns of discrimination. Drawing from social psychology, we examine how VLMs connect visual identity cues to trait and role-based inferences, encoding social hierarchies, through biased selections. Our findings uncover subtle, multifaceted, and surprising stereotypical patterns, offering insights into how VLMs construct social meaning from inputs.
Code (1)
Tasks
Decision MakingQuestion AnsweringVisual Question Answering (VQA)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The Need for a Socially-Grounded Persona Framework for User Simulation
Synthetic personas are widely used to condition large language models (LLMs) for social simulation, yet most personas are still constructed from coarse sociodemographic attributes or summaries. We revisit persona creatio…
Enabling Scalable Evaluation of Bias Patterns in Medical LLMs
Large language models (LLMs) have shown impressive potential in helping with numerous medical challenges. Deploying LLMs in high-stakes applications such as medicine, however, brings in many concerns. One major area of c…
Knowledge GraphsSpecificityYou Never Know: Quantization Induces Inconsistent Biases in Vision-Language Foundation Models
We study the impact of a standard practice in compressing foundation vision-language models - quantization - on the models' ability to produce socially-fair outputs. In contrast to prior findings with unimodal models tha…
QuantizationSerendipity with Generative AI: Repurposing knowledge components during polycrisis with a Viable Systems Model approach
Organisations face polycrisis uncertainty yet overlook embedded knowledge. We show how generative AI can operate as a serendipity engine and knowledge transducer to discover, classify and mobilise reusable components (mo…
Misaligned by Reward: Socially Undesirable Preferences in LLMs
Reward models are a key component of large language model alignment, serving as proxies for human preferences during training. However, existing evaluations focus primarily on broad instruction-following benchmarks, prov…