使用社交媒体作为人工智能模型的训练数据的伦理,用于数字表型化
Aditi Jaiswal1, Aekta Shah2, Christopher Harjadi3
1Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States.
JMIR formative research
|July 17, 2024
概括
研究人员强调使用社交媒体数据用于数字表型AI模型的伦理问题. 用户明确的同意至关重要,特别是对于神经多样性标签等敏感健康数据,倡导以社区为中心的AI开发.
科学领域:
- 数字健康数字健康
- 人工智能的人工智能
- 生物伦理学生物伦理学
背景情况:
- 数字表型化使用个人传感技术来量化医疗保健中的人类特征.
- 社交媒体平台越来越多地被探索为AI在数字表型化中的数据来源.
- 围绕数据隐私和同意的伦理考虑在这个领域正在迅速发展.
研究的目的:
- 讨论使用社交媒体数据进行人工智能驱动的数字表型化的新兴伦理问题.
- 强调用户对社交媒体数据的明确同意的必要性,特别是敏感的健康信息.
- 在开发医疗保健人工智能模型时,倡导基于社区的参与式设计.
主要方法:
- 对数字表型研究中的伦理考虑的评论.
- 从社交媒体获取人工智能模型培训数据的分析.
- 审查同意要求和参与式设计原则.
主要成果:
- 确定了需要社交媒体用户明确同意的关键伦理需求.
- 突出了与抓取敏感数据相关的风险,例如神经多样性标签.
- 建议以社区为基础的参与性设计作为负责任的发展方法.
结论:
- 在利用社交媒体数据进行数字表型AI时,明确的同意至关重要.
- 保护敏感用户信息,如神经多样性标签,需要严格的道德准则.
- 基于社区的参与式设计原则应指导医疗保健AI使用社交媒体数据的伦理发展.
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