走向基于社区的自然语言处理 (CBNLP):与社区合作
Malvika Pillai1,2, Ashley C Griffin2,3, Clair A Kronk4
1Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, United States.
Journal of medical Internet research
|August 4, 2023
概括
基于社区的参与式研究可以指导自然语言处理 (NLP) 工具在医疗保健中的公平发展. 参与社区利益相关者确保文化考虑,并减轻NLP应用中的历史不公正.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 基于社区的研究.
背景情况:
- 自然语言处理 (NLP) 正在迅速发展,创造了创新的医疗保健应用.
- 人们对当前NLP工具的历史不公正和文化不敏感存在担忧.
- 传统的医疗保健NLP开发往往缺乏社区利益相关者与生活经验的投入.
研究的目的:
- 检查公平的方法,让社区参与医疗保健NLP工具开发.
- 为NLP适应社区参与型研究的原则.
主要方法:
- 借鉴基于社区的参与式研究 (CBPR) 框架.
- 确定在NLP中有效的社区协作机制和资源.
主要成果:
- 基于社区的NLP (CBNLP) 需要仔细考虑协作过程.
- 与社区成员的有效接触对于伦理和有影响力的NLP至关重要.
结论:
- 医疗保健NLP的公平发展需要整合社区观点.
- 基于社区的NLP (CBNLP) 提供了一条缓解偏见和增强文化相关性的途径.
- 与社区利益相关者进行战略合作对于最大限度地发挥NLP工具的社会和伦理益处至关重要.
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