How Reasoning Influences Intersectional Biases in Vision Language Models

TL;DR

This paper investigates how reasoning processes in vision language models contribute to intersectional social biases, revealing that biased reasoning underpins disparities in occupation prediction tasks and emphasizing the importance of aligning model reasoning with human values.

Contribution

It systematically analyzes reasoning and biases in open-source VLMs across multiple occupations and prompting styles, highlighting the link between reasoning patterns and social disparities.

Findings

Biased reasoning patterns underlie intersectional disparities.

Different prompting styles influence model predictions and reasoning.

Biases are systematically present across multiple open-source VLMs.

Abstract

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.