偏见减轻的人工智能作为弹性和有效卫生系统的基础
Jarbas Barbosa da Silva1, Maureen Birminghamm1, Ana Rivière Cinnamond1
1World Health Organization Regional Office for the Americas, 525 23rd St NW, Washington, DC, 20037, United States, 1 2029743301.
医疗保健人工智能 (AI) 的算法偏见可能会加剧健康差异. 解决这种偏见对于质量护理至关重要,需要一种超越技术修复的治理方法,以确保人工智能工具有利于所有人群.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 健康 公平 卫生 公平
背景情况:
- 人工智能 (AI) 正在改变医疗保健,但有偏见的算法有可能使健康差异延续.
- 人工智能培训数据集中的数据表示不均,限制了不同人群的准确性和通用性.
- 医疗保健中的算法偏见人工智能必须被认为是一个关键的质量和安全问题.
研究的目的:
- 将与健康相关的AI中的算法偏见作为卫生系统的质量,安全和治理挑战.
- 为决策者,监管者,卫生系统领导者和开发者提供有关操作化的偏见缓解的信息.
- 为美洲提供一个以区域为基础的政策视角,考虑低收入和中等收入环境.
主要方法:
- 综合现有科学证据和监管指导.
- 在AI生命周期中概述算法偏差 (表示,测量,聚合,部署) 的形式.
- 提出一个以治理为导向的框架,用于从设计到市场后监测的偏差缓解.
主要成果:
- 识别了算法偏见的形式及其在AI生命周期中的出现.
- 在更广泛的数字健康和社会经济背景下,存在技术偏见的挑战.
- 制定了一个全面的治理框架,以减轻健康方面的偏见.
结论:
- 医疗保健中的算法偏见人工智能是一个系统质量和治理问题,而不仅仅是技术问题.
- 有效的偏见缓解需要跨越整个AI生命周期的多利益相关方方法.
- 将公平作为卫生系统绩效的可衡量的组成部分,对于公平的AI部署至关重要.
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