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Updated: May 15, 2025

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重新审视技术偏差缓解策略
Abdoul Jalil Djiberou Mahamadou1, Artem A Trotsyuk1
1Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California, USA; email: abdjiber@stanford.edu, atrotsyuk@stanford.edu.
Annual review of biomedical data science
|April 8, 2025
概括
在医疗保健中对人工智能 (AI) 偏差的技术解决方案面临实际限制. 本综述分析了这些局限性,并提出了价值敏感的人工智能,以确保对不同人群的公平.
科学领域:
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
- 人工智能伦理学
背景情况:
- 人工智能 (AI) 偏差缓解主要依赖于技术解决方案.
- 现有的审查往往忽视了这些解决方案在现实世界医疗保健环境中的实际实施挑战.
研究的目的:
- 批判性地分析技术AI偏差缓解策略在医疗保健中的实际局限性.
- 确定影响人工智能公平解决方案在现实世界中实施的关键维度.
- 提出价值敏感的人工智能作为利益相关者参与和价值体现的框架.
主要方法:
- 在五个关键方面对AI偏见缓解局限性的结构化分析:偏见/公平的定义,战略选择,开发阶段,人口适用性和上下文设计.
- 用从医疗保健和生物医学应用中的经验研究来说明局限性.
- 讨论价值敏感的人工智能框架及其应用.
主要成果:
- 技术AI偏差缓解策略在医疗保健中面临重大实际限制.
- 关键的挑战包括定义偏见/公平性,选择兼容的策略,确定最佳的实施阶段,确保特定人口的适用性,并适应上下文细微差别.
- 价值敏感的人工智能提供了一个有希望的方法,通过整合利益相关者的价值来解决这些局限性.
结论:
- 仅靠技术解决方案就不足以缓解医疗保健中的AI偏见.
- 对实际实施挑战的更深入的理解对于开发有效的AI公平战略至关重要.
- 采用价值敏感的人工智能原则可以使医疗保健中的人工智能系统更公平,更值得信赖.
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