How Reasoning Influences Intersectional Biases in Vision Language Models
This work addresses the problem of social biases in VLMs for users deploying these models in real-world applications, but it is incremental as it builds on existing bias analysis methods.
The study analyzed social biases in five open-source Vision Language Models (VLMs) on an occupation prediction task using the FairFace dataset, finding that biased reasoning patterns underlie intersectional disparities, which can adversely affect downstream performance.
Vision Language Models (VLMs) are increasingly deployed across downstream tasks, yet their training data often encode social biases that surface in outputs. Unlike humans, who interpret images through contextual and social cues, VLMs process them through statistical associations, often leading to reasoning that diverges from human reasoning. By analyzing how a VLM reasons, we can understand how inherent biases are perpetuated and can adversely affect downstream performance. To examine this gap, we systematically analyze social biases in five open-source VLMs for an occupation prediction task, on the FairFace dataset. Across 32 occupations and three different prompting styles, we elicit both predictions and reasoning. Our findings reveal that the biased reasoning patterns systematically underlie intersectional disparities, highlighting the need to align VLM reasoning with human values prior to its downstream deployment.