Mitigating the Risk of Health Inequity Exacerbated by Large Language Models

• URL: http://arxiv.org/abs/2410.05180v2
• Date: Mon, 14 Oct 2024 14:27:34 GMT
• Title: Mitigating the Risk of Health Inequity Exacerbated by Large Language Models
• Authors: Yuelyu Ji, Wenhe Ma, Sonish Sivarajkumar, Hang Zhang, Eugene Mathew Sadhu, Zhuochun Li, Xizhi Wu, Shyam Visweswaran, Yanshan Wang,
• Abstract summary: We show that incorporating non decisive sociodemographic factors into the input of large language models can lead to incorrect and harmful outputs. We introduce EquityGuard, a novel framework designed to detect and mitigate the risk of health inequities in LLM based medical applications.
• Score: 5.02540629164568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in large language models have demonstrated their potential in numerous medical applications, particularly in automating clinical trial matching for translational research and enhancing medical question answering for clinical decision support. However, our study shows that incorporating non decisive sociodemographic factors such as race, sex, income level, LGBT+ status, homelessness, illiteracy, disability, and unemployment into the input of LLMs can lead to incorrect and harmful outputs for these populations. These discrepancies risk exacerbating existing health disparities if LLMs are widely adopted in healthcare. To address this issue, we introduce EquityGuard, a novel framework designed to detect and mitigate the risk of health inequities in LLM based medical applications. Our evaluation demonstrates its efficacy in promoting equitable outcomes across diverse populations.

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