Mitigating the Risk of Health Inequity Exacerbated by Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929540116250624 |
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| author | Ji, Yuelyu Ma, Wenhe Sivarajkumar, Sonish Zhang, Hang Sadhu, Eugene Mathew Li, Zhuochun Wu, Xizhi Visweswaran, Shyam Wang, Yanshan |
| author_facet | Ji, Yuelyu Ma, Wenhe Sivarajkumar, Sonish Zhang, Hang Sadhu, Eugene Mathew Li, Zhuochun Wu, Xizhi Visweswaran, Shyam Wang, Yanshan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mitigating the Risk of Health Inequity Exacerbated by Large Language Models Ji, Yuelyu Ma, Wenhe Sivarajkumar, Sonish Zhang, Hang Sadhu, Eugene Mathew Li, Zhuochun Wu, Xizhi Visweswaran, Shyam Wang, Yanshan Computation and Language 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. |
| title | Mitigating the Risk of Health Inequity Exacerbated by Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.05180 |