Building models, building capacity: A review of participatory machine learning for HIV prevention
Open accessAbstract
A growing number of researchers and practitioners are embracing a “participatory turn” in machine learning (ML) to improve model development, prevent harm, and provide communities more influence over systems that impact them. In this paper, we explore the intersection of participatory practices in healthcare and the emerging focus on responsible AI with a focus on human immunodeficiency virus (HIV) care. We review the historical context of participation in HIV treatment and prevention, emphasizing how patient activism has shaped practices in this field. We then review participatory ML in HIV prevention and present a brief case study of a project designed to identify candidates for pre-exposure prophylaxis (PrEP) in Louisiana. The review highlights the essential steps in conducting participatory ML. Finally, we draw lessons for future participatory ML projects, underscoring the importance of long-term collaboration, responsiveness to partner feedback, and the crucial role of capacity-building for individuals and organizations. Effective participation requires substantial resources and investment, which supports overall project goals beyond mere improvements in model performance. We also draw lessons for advancing the participatory ML field, including (1) the impact of funding mandates on fostering effective engagement; (2) the need to scale participatory processes rather than just technologies; and (3) the need for genuine participation to allow flexibility in project plans, timelines, and shifts in institutional power dynamics.
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Qualitative methods are widely used to explore stakeholders' experiences, challenges, and needs in the adoption of AI.
“Qualitative methods are widely used to explore stakeholders’ experiences, challenges, and needs in the adoption of AI37–39.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [39]
Section 2.3 describes focus groups examining patients’ views of an HIV-prediction AI system and clinicians’ concerns about its implementation. The HEROIC case study in section 2.4 also reports interviews about workflow and implementation needs. These examples support the use of qualitative methods for that purpose, but do not establish that they are widely used across AI adoption generally. The indexed copy provides no page numbers for these passages. (LLM, conf=0.85)
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Cited by
Sorted by citations
Establishing organizational AI governance in healthcare: a case study in Canada
Kim, J. Y., Hasan, A., Kueper, J., Tang, T., Hayes, C., Fine, B., Balu, S., & Sendak, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01909-3 · Cited by 42 · see citing works
Self-citationHumanizing AI in Health: the ethics challenges Outcomes of stakeholder engagements in India
Vaz, M., Shah, N., Baveja, A., Raj, T. D. S., & Bhan, A. (2026) · Wellcome Open Research · doi:10.12688/wellcomeopenres.24986.1
- Authors
- Sendak, M., Young, M., Kim, J. Y., Hasan, A., Kelsey, C., O’Neal, C., Jagneaux, T., Wilbright, W., Couk, J., Lim, S., Davenport, T., Lolis, S., Thomas, J., Widman, S., Balu, S., Clement, M., & Okeke, L.
- Affiliations
- Duke University (US)
- Data & Society Research Institute (US)
- Louisiana State University Health Sciences Center New Orleans (US)
- Corresponding author
- Kim, Jee Young
- ORCID
- Venue
- PLOS Global Public Health
- Publisher
- Public Library of Science (PLoS)
- Published
- 2025-06-04
- Volume
- 5
- Issue
- 6
- Pages
- e0003862
- ISSN
- 2767-3375 (ISSN-L)
- PMC
- PMC12136320
- OpenAlex
- W4411022086
- Cited by
- 2
- References
- 46
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 0.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLOS Global Public Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases (5R01AI169641)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Infectious Diseases › HIV/AIDS Research and Interventions
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- HIV prevention, pre-exposure prophylaxis, responsible AI, patient activism, capacity building, community participation, health equity, Louisiana, power relations
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