通过社区参与人工智能驱动的预防科学,解决健康差距
Emily E Haroz1, Novalene Goklish2, Adrienne Dillard3
1Center for Indigenous Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA. eharoz1@jhu.edu.
概括
社区参与对于在预防科学中开发无偏见的人工智能和机器学习 (AI/ML) 工具至关重要. 结合社区的投入,确保AI/ML应用程序是公平的,有效地服务于多样化的群体.
科学领域:
- 公共卫生 公共卫生
- 健康 公平 卫生 公平
- 医疗保健中的人工智能
背景情况:
- 人工智能和机器学习 (AI/ML) 具有加剧或减轻健康不平等的潜力.
- 社区参与是一项拟议的战略,旨在减轻AI/ML工具中的偏见.
- 整合社区观点对于公平的AI/ML发展至关重要.
研究的目的:
- 概述整个AI/ML模型开发和实施生命周期中将社区参与纳入.
- 为了证明社区参与塑造AI/ML预防科学中的价值.
- 提供社区参与的AI/ML在健康公平倡议中的实例.
主要方法:
- 从预防研究阶段调整框架,以指导社区参与AI/ML.
- 在社区参与下,对现实世界AI/ML应用的案例研究分析.
- 社区驱动的AI/ML开发过程的定性综合.
主要成果:
- 社区参与可以在AI/ML开发和实施的各个阶段进行整合.
- 例如,与土著社区一起预防自杀,为西班牙裔/拉丁裔人口预防慢性疾病,以及为夏威夷原住民分配资源.
- 社区驱动的AI/ML努力在解决健康的社会决定因素方面显示出前景.
结论:
- 社区参与至关重要,以确保AI/ML工具是相关的,严格的,并达到服务不足的人群.
- 在AI/ML开发中优先考虑社区声音对于推进健康公平至关重要.
- 合作方法是必要的,以负责任地利用AI/ML在预防科学中.
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