为了捍卫同情的信息学
Harry Hochheiser1, Shyam Visweswaran1
1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15217, United States.
概括
最近对多样性,公平性和包容性 (DEI) 讨论的限制可能会阻碍生物医学信息学研究,特别是在AI偏见和公平性方面. 团结一致的社区反应对于支持 DEI 在现场至关重要.
科学领域:
- 生物医学信息学 生物医学信息学
- 健康 公平 研究 健康 公平 研究
- 人工智能伦理学
背景情况:
- 最近的美国联邦行政命令引入了影响多样性和性别讨论的限制.
- 这些限制对生物医学信息学领域构成潜在的挑战.
研究的目的:
- 探索最近政府对多样性,公平性和包容性 (DEI) 限制在生物医学信息学中的影响.
- 评估与人工智能和机器学习中的公平性和偏见相关的研究的潜在影响.
主要方法:
- 分析美国联邦有关多样性和性别问题的行政命令.
- 讨论对生物医学信息学研究和实践的影响.
主要成果:
- 对术语的限制可能会阻碍对临床AI和机器学习算法的偏见和公平等关键问题的研究.
- 局限性可能会减少信息学研究的范围,并阻碍努力增加该领域的多样化视角.
结论:
- 需要一个统一的社区反应来解决对生物医学信息学DEI研究的威胁.
- 像美国医学信息学协会 (AMIA) 这样的组织可以促进支持DEI的集体立场.
- 信息学界必须明确支持多样性,公平性和包容性,以支持代表性不足的观点.
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