超越人工智能精神病:大型语言模型相关的精神病现象的功能类型学
Matthew Flathers1, Spencer Roux1, John Torous2
1Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA.
人工智能精神病不是一个单一的情况. 一个新的类型学将大型语言模型 (LLM) 相互作用分类为催化剂,放大器,共同作者或对象,以指导临床和技术干预.
科学领域:
- 精神病学是一个精神病学.
- 计算机科学 计算机科学
- 人与计算机的交互
背景情况:
- 最近的案例将大型语言模型 (LLM) 相互作用与严重的心理困扰和精神病联系起来.
- 像"AI精神病"这样的现有术语过度简化了各种现象.
- 需要一种多学科的方法来理解和解决这些复杂的相互作用.
研究的目的:
- 为LLM相关的精神病现象提出一个功能类型学.
- 要区分LLM在心理困境中扮演的各种角色.
- 为有针对性的临床和技术干预提供信息.
主要方法:
- 分析涉及LLM相互作用和心理结果的不同案例研究.
- 基于LLM在观察到的现象中的作用,开发一个功能类型学.
- 软件工程师,精神分裂症患者和精神病学家之间的合作.
主要成果:
- 拟议的类型学将LLM角色分类为:催化剂,放大器,共同作者或对象.
- 催化剂:LLMs在健康个体中加速产生新的症状.
- 放大器:LLM恶化了先前存在的精神疾病.
- 共同作者:LLM参与开发有害叙事.
- 对象:LLM成为妄想信念的焦点.
结论:
- 区分LLM的功能作用对于准确的诊断和治疗至关重要.
- 这种类型学有助于临床医生识别有关LLM的使用模式.
- 技术公司可以根据这些功能角色制定具体的保障措施.
- 超越人听闻,转向特定机制的干预是必不可少的.
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