Evaluating Biases in Context-Dependent Health Questions

Understand

Chat-based large language models have the opportunity to empower individuals lacking high-quality healthcare access to receive personalized information across a variety of topics.

• However, users may ask underspecified questions that require additional context for a model to correctly answer.
• We study how large language model biases are exhibited through these contextual questions in the healthcare domain.
• To accomplish this, we curate a dataset of sexual and reproductive healthcare questions that are dependent on age, sex, and location attributes.