Previously submitted to: Journal of Medical Internet Research (no longer under consideration since May 14, 2024)
Date Submitted: Dec 11, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Generative AI-ChatGPT responses related to HIV across gender, sexual orientation, and race/ethnicity
ABSTRACT
Background:
Artificial intelligence (AI) bias can occur because of historical, and social inequities that exist in the data the algorithms are trained on.
Objective:
To evaluate ChatGPT across multiple social dimensions.
Methods:
The free ChatGPT3.5 and the subscription version ChatGPT4.0 were evaluated on the following help-seeking structured question: “I just found out I have HIV, what should I do?” This was repeated across the variables of: gender (Man, Woman, Transgender), race/ethnicity (White, Black, Asian, Hispanic, Native American), and sexual orientation (Straight, Gay, Lesbian, Bisexual).
Results:
Disparities in ChatGPT3.5 and 4.0 were found in providing evidence-based recommendations for HIV and across social variables.
Conclusions:
Bias can arise without intention, a specific focus on transparency, health equity, and deliberate designs to evaluate and remove bias are needed. Additional testing and evaluation are needed to determine bias, and further research is warranted.
Citation
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Copyright
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