A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
This work addresses health equity issues in AI for healthcare, providing tools to identify biases in LLMs, though it is incremental as it builds on existing evaluation methods without claiming holistic solutions.
The researchers tackled the problem of evaluating health equity harms and biases in large language models (LLMs) by developing a multifactorial framework and datasets for assessing biases in medical answers, finding that their approach surfaces biases missed by narrower evaluations. They conducted a case study with Med-PaLM 2, emphasizing the importance of diverse methodologies and rater backgrounds.
Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. We present resources and methodologies for surfacing biases with potential to precipitate equity-related harms in long-form, LLM-generated answers to medical questions and conduct a large-scale empirical case study with the Med-PaLM 2 LLM. Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases, and EquityMedQA, a collection of seven datasets enriched for adversarial queries. Both our human assessment framework and dataset design process are grounded in an iterative participatory approach and review of Med-PaLM 2 answers. Through our empirical study, we find that our approach surfaces biases that may be missed via narrower evaluation approaches. Our experience underscores the importance of using diverse assessment methodologies and involving raters of varying backgrounds and expertise. While our approach is not sufficient to holistically assess whether the deployment of an AI system promotes equitable health outcomes, we hope that it can be leveraged and built upon towards a shared goal of LLMs that promote accessible and equitable healthcare.