A toolbox for surfacing health equity harms and biases in large language models
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 our 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 by 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 artificial intelligence (AI) system promotes equitable health outcomes, we hope that it can be leveraged and built upon toward a shared goal of LLMs that promote accessible and equitable healthcare.
Authors
- Christopher Semturs (ORCID: https://orcid.org/0000-0001-6108-2773)
- Jamila Smith-Loud (ORCID: https://orcid.org/0009-0007-7403-7358)
- Awa Dieng
- Katherine Heller (ORCID: https://orcid.org/0000-0002-4848-7466)
- Qazi Mamunur Rashid
- Stephen Pfohl (ORCID: https://orcid.org/0000-0003-0551-9664)
- Ivor B. Horn (ORCID: https://orcid.org/0000-0002-5571-4671)
- Mike Schaekermann (ORCID: https://orcid.org/0000-0002-1735-9680)
- Darlene Neal
- Leo Anthony Celi (ORCID: https://orcid.org/0000-0001-6712-6626)
- Sushant Prakash (ORCID: https://orcid.org/0009-0000-4162-4600)
- Akeiylah Dewitt (ORCID: https://orcid.org/0000-0002-4534-7273)
- Chirag Nagpal (ORCID: https://orcid.org/0000-0003-2212-5392)
- Joelle K. Barral (ORCID: https://orcid.org/0009-0009-0432-5148)
- Shekoofeh Azizi (ORCID: https://orcid.org/0000-0002-7447-6031)
- Nenad Tomašev (ORCID: https://orcid.org/0000-0003-1624-0220)
- Rory Sayres (ORCID: https://orcid.org/0000-0002-8269-5779)
- Yossi Matias (ORCID: https://orcid.org/0000-0003-3960-6002)
- Greg S. Corrado (ORCID: https://orcid.org/0000-0001-8817-0992)
- Heather Cole-Lewis (ORCID: https://orcid.org/0000-0002-7275-1810)
- Mercy Asiedu (ORCID: https://orcid.org/0000-0002-0230-5022)
- Negar Rostamzadeh (ORCID: https://orcid.org/0000-0002-9638-4664)
- Yun Liu (ORCID: https://orcid.org/0000-0003-4079-8275)
- Liam G. McCoy (ORCID: https://orcid.org/0000-0002-4468-2256)
- Alicia Parrish (ORCID: https://orcid.org/0000-0002-1054-0516)
- K. K. Singhal (ORCID: https://orcid.org/0009-0001-0286-609X)
- P. Mansfield (ORCID: https://orcid.org/0000-0003-4969-0543)
- Alan Karthikesalingam (ORCID: https://orcid.org/0009-0000-4958-5976)
- Alanna Walton
- Preeti Singh (ORCID: https://orcid.org/0009-0001-5871-4992)
Institutions
- Google (United States) (US)
- Beth Israel Deaconess Medical Center (US)
- Harvard University (US)
- University of Alberta (CA)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Nature Medicine
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1038/s41591-024-03258-2
- Citations
- 65
- Primary Topic
- Risk Perception and Management
- Type
- article
- Field-Weighted Citation Impact
- 73.58
Funders
- National Science Foundation