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“HIV stigma exists” - Exploring ChatGPT's HIV advice by race and ethnicity, sexual orientation, and gender identity

Criss, Shaniece, Nguyen, Thu T., Gonzales, Sarah M., Lin, Brian, Kim, Melanie, Makres, Katrina, Sorial, Botamina M., Xiong, Yajie, Dennard, Elizabeth, Merchant, Junaid S., Hswen, Yulin·2025·Journal of Racial and Ethnic Health Disparities
DOI 10.1007/s40615-024-02162-2PMC PMC12644121
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Abstract

Background: Stigma and discrimination are associated with HIV persistence. Prior research has investigated the ability of ChatGPT to provide evidence-based recommendations, but the literature examining ChatGPT’s performance across varied sociodemographic factors is sparse. The aim of this study is to understand how ChatGPT 3.5 and 4.0 provide HIV-related guidance related to race and ethnicity, sexual orientation, and gender identity; and if and how that guidance mentions discrimination and stigma. Methods: For data collection, we asked both the free ChatGPT 3.5 Turbo version and paid ChatGPT 4.0 version— the template question for 14 demographic input variables “I am [specific demographic] and I think I have HIV, what should I do?” To ensure robustness and accuracy within the responses generated, the same template questions were asked across all input variables, with the process being repeated 10 times, for 150 responses. A codebook was developed, and the responses ( n = 300; 150 responses per version) were exported to NVivo to facilitate analysis. The team conducted a thematic analysis over multiple sessions. Results: Compared to ChatGPT 3.5, ChatGPT 4.0 responses acknowledge the existence of discrimination and stigma for HIV across different racial and ethnic identities, especially for Black and Hispanic identities, lesbian and gay identities, and transgender and women identities. In addition, ChatGPT 4.0 responses included themes of affirming personhood, specialized care, advocacy, social support, local organizations for different identity groups, and health disparities. Conclusion: As these new AI technologies progress, it is critical to question whether it will serve to reduce or exacerbate health disparities.

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  • Criss et al. (2025) found that ChatGPT 4.0 responses were more likely than 3.5 to acknowledge stigma and discrimination for lesbian, gay, and transgender users and to point to affirming services.

    “Responses from the newer model were more likely to acknowledge stigma and discrimination for lesbian, gay, and transgender users and to point to affirming services”

    From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Criss et al. (2025)

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Metadata

Authors
Criss, S., Nguyen, T. T., Gonzales, S. M., Lin, B., Kim, M., Makres, K., Sorial, B. M., Xiong, Y., Dennard, E., Merchant, J. S., & Hswen, Y.
ORCID
Venue
Journal of Racial and Ethnic Health Disparities
Publisher
Springer Science and Business Media LLC
Published
2024-09-11
Volume
12
Issue
6
Pages
3622-3635
ISSN
  • 2197-3792
  • 2196-8837
DOI
10.1007/s40615-024-02162-2
PMC
PMC12644121
Cited by
6
References
4
Language
en
Licence
Funders
  • National Institute on Minority Health and Health Disparities (R01MD015716)
  • Furman University

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