减少医疗保健人工智能的偏见:白皮书
Carolyn Sun1, Shannon L Harris2
1Hunter-Bellevue School of Nursing, Hunter College, New York, NY, USA.
Health informatics journal
|November 14, 2024
概括
在人工智能 (AI) 中减少种族主义的策略对于改善健康公平至关重要. 专家们确定了关键主题,包括减少数据集偏差,准确建模,人工智能透明度,监管和利益相关方参与等.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能伦理学 人工智能伦理学
- 健康 公平 卫生 公平
背景情况:
- 人工智能 (AI) 中的种族主义对健康结果产生了负面影响.
- 目前缺乏关于减轻医疗保健中的AI偏见的共识.
- 解决医疗保健中的偏见人工智能是研究和政策的关键领域.
研究的目的:
- 识别和评估减少医疗保健中的种族主义战略.
- 综合专家意见和有关AI偏见缓解的现有文献.
- 建立一个框架,在医学中开发公平的AI.
主要方法:
- 在2022年召开国际专家会议.
- 促进了关于减少医疗保健偏见的策略的讨论.
- 审查了有关AI偏见缓解技术的现有文献.
主要成果:
- 确定了减少人工智能种族主义的五个主要主题:数据集偏差减少,准确的数据建模,人工智能透明度,人工智能和开发者的监管以及利益相关者的参与.
- 概述了各种偏见缓解策略的优缺点.
- 综合了当前关于人工智能在医疗保健中的公平性实践方法的知识.
结论:
- 在数据,建模,透明度和监管方面实施战略对于公平的AI至关重要.
- 多方利益相关者合作对于成功缓解人工智能偏见至关重要.
- 需要进一步的研究和政策制定,以确保人工智能促进健康公平.
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