Evaluating Biases in Context-Dependent Health Questions
This work addresses fairness issues in AI for healthcare access, but it is incremental as it focuses on a specific domain and dataset.
The study investigated biases in large language models when answering underspecified health questions requiring demographic context, finding that models favored young adult female users in responses to sexual and reproductive healthcare queries.
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. We compare models' outputs with and without demographic context to determine group alignment among our contextual questions. Our experiments reveal biases in each of these attributes, where young adult female users are favored.