对健康中的公平性进行科学度分析AI文献
Isabelle Rose I Alberto1, Nicole Rose I Alberto1, Yuksel Altinel2
1University of the Philippines College of Medicine, Manila, Philippines.
PLOS global public health
|January 19, 2024
概括
人工智能公平性研究中缺乏多样性. 研究表明,来自高收入国家的作者,主要是白人和男性,代表人数过多,影响AI.
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
- 科学社会学科学社会学
背景情况:
- 人工智能 (AI) 和机器学习越来越多地成为现代医疗保健的组成部分.
- 人工智能公平性和医疗应用中的潜在偏见越来越令人担忧.
- 研究人员的多样性对医学AI公平性辩论的贡献被低估了.
研究的目的:
- 调查发表关于人工智能在医疗保健中的公平性学术作者的人口多样性.
- 分析作者人口统计,地理来源和研究影响 (引用,期刊影响) 之间的关系.
主要方法:
- 在PubMed,Google Scholar和Embase系统地搜索文献,寻找有关AI在医学中的公平性文章.
- 手动的资格选,数据提取和交叉检查.
- 图书统计分析包括空间图 (Tableau) 和统计分析 (Python,Matplotlib,Seaborn) 的作者人口统计,资金和引用数量.
主要成果:
- 确定了375个符合条件的关于医疗保健人工智能公平性的出版物.
- 发现来自高收入国家的白人男性作者的过度代表,特别是在主导和高级作者的职位上.
- 来自高收入国家的研究表明,在影响力更高的期刊上引用率和出版率更高.
结论:
- 医学人工智能公平领域缺乏作者多样性,来自高收入国家的作者占主导地位,他们是白人和男性.
- 这种缺乏多样性可能会破坏在AI公平性研究中实现公正性的目标.
- 解决作者多样性对于确保人工智能在医疗保健中的公平发展和应用至关重要.
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