模拟错误使用大型语言模型和临床信用系统
James T Anibal1, Hannah B Huth2, Jasmine Gunkel3
1Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, MD, USA. anibal.james@nih.gov.
NPJ digital medicine
|November 11, 2024
概括
医疗保健中的大型语言模型 (LLM) 可能会因为偏见的培训数据而优先考虑社会利益而不是个人权利. 这项研究强调了人工智能驱动的社会信用系统和不公平的资源分配的风险.
科学领域:
- 人工智能的人工智能
- 医疗保健技术 技术 医疗保健 技术
- 生物伦理学生物伦理学
背景情况:
- 大型语言模型 (LLM) 显示了推进医疗保健服务的潜力.
- 然而,存在与滥用和固有的偏见相关的重大风险.
- 在多样化的数据集上训练的LLM可能会使社会不平等继续存在.
研究的目的:
- 调查大语言模型 (LLM) 在应用于医疗保健资源分配时的潜在偏差.
- 分析LLM在多式联络数据 (包括金融,互联网和社会行为信息) 上接受培训的风险.
- 评估LLM偏见对个人权利与集体利益以及人工智能驱动的社会信用系统的潜在影响.
主要方法:
- 对LLM培训方法和数据源的分析.
- 在模拟的医疗保健场景中评估算法决策过程.
- 审查有关人工智能的伦理框架在资源分配和社会评分.
主要成果:
- 法律法人表现出倾向于偏见,偏爱集体或系统利益而不是个人权利.
- 包括金融,互联网和社会行为在内的训练数据可以嵌入不公正的标准.
- 该研究确定了LLM促进AI驱动社会信用系统发展的重大风险.
结论:
- 在医疗保健中应用LLMs需要仔细考虑道德含义和潜在偏见.
- 保护措施是必要的,以防止滥用LLMs进行歧视性资源分配和社会控制.
- 进一步的研究至关重要,以确保人工智能开发与正义原则和医疗保健中的个人权利保持一致.
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