在LLM驱动的精神病学中,非典型性的问题
Bosco Garcia1, Eugene Y S Chua2, Harman Singh Brah3,4
1Philosophy, University of California San Diego, La Jolla, California, USA jgarca@ucsd.edu.
Journal of medical ethics
|August 8, 2025
概括
大型语言模型 (LLM) 提供心理健康解决方案,但由于精神病患者的"非典型性"而带来风险. 一个新的框架,动态上下文认证 (DCC),通过优先考虑安全而不是性能来管理这些风险.
科学领域:
- 精神病学是一个精神病学.
- 人工智能的人工智能
- 医学伦理 医学伦理
背景情况:
- 针对全球心理健康挑战,提出了大型语言模型 (LLM).
- 在精神病学部署的法学士提出了关于伦理方面的担忧精神病学部署.
研究的目的:
- 为解决在精神病学背景下LLM部署中不合时宜的伦理挑战.
- 为在精神卫生保健中安全有效地使用LLM提出一个新的框架.
主要方法:
- 对用于精神病学应用的标准LLM缓解策略 (提示工程,微调) 的批评.
- 动态上下文认证 (DCC) 的建议:一个分阶段,可逆和上下文敏感的框架.
- 灵感来自临床翻译和AI治理安全模型.
主要成果:
- 标准的LLM缓解策略不足以管理精神病患者的非典型性.
- 动态上下文认证 (DCC) 提供了一种积极的方法来管理与非典型性相关的风险.
- 在LLM部署中,DCC优先考虑解释性安全,而不是静态性能指标.
结论:
- 精神病患者的非典型性对LLM部署具有结构性风险.
- 动态上下文认证 (DCC) 为管理精神病学LLM相关风险提供了一个可行的框架.
- 积极管理非典型性对于LLM在精神卫生保健中的伦理整合至关重要.
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