BiasICL: In-Context Learning and Demographic Biases of Vision Language Models
Journal:
arXiv
Published Date:
Mar 4, 2025
Abstract
Vision language models (VLMs) show promise in medical diagnosis, but their
performance across demographic subgroups when using in-context learning (ICL)
remains poorly understood. We examine how the demographic composition of
demonstration examples affects VLM performance in two medical imaging tasks:
skin lesion malignancy prediction and pneumothorax detection from chest
radiographs. Our analysis reveals that ICL influences model predictions through
multiple mechanisms: (1) ICL allows VLMs to learn subgroup-specific disease
base rates from prompts and (2) ICL leads VLMs to make predictions that perform
differently across demographic groups, even after controlling for
subgroup-specific disease base rates. Our empirical results inform
best-practices for prompting current VLMs (specifically examining demographic
subgroup performance, and matching base rates of labels to target distribution
at a bulk level and within subgroups), while also suggesting next steps for
improving our theoretical understanding of these models.
