走向人类-人工智能决策合作的科学:一个互补框架
Cleotilde Gonzalez1,2, Kate Donahue3, Daniel G Goldstein4
1Social and Decision Sciences Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
PNAS nexus
|March 16, 2026
概括
这项研究探讨了人 - 人工智能互补性,团队表现优于个人. 它提供了一个框架和设计原则,以有效,以人为中心的人工智能协作在关键决策.
科学领域:
- 认知科学 认知科学
- 人工智能 (AI) 是一种人工智能.
- 人类因素 人类因素
- 组织行为 组织行为
- 伦理学 伦理学 伦理学
背景情况:
- 人工智能 (AI) 越来越多地成为健康,安全,金融和治理领域关键决策过程的组成部分.
- 主要的挑战已经从人类-人工智能协作转移到为最佳互补性构建这种互动.
- 人类-人工智能互补意味着一个协同关系,人类-人工智能组合的团队超过了人类或人工智能独立运作的表现.
研究的目的:
- 推进人类-人工智能决策合作的科学.
- 根据集体智能和核心认知过程,提出理解和设计有效的人类-人工智能团队的框架.
- 确定社会技术因素和设计原则对于实现人类-人工智能互补至关重要.
主要方法:
- 从认知科学,人工智能,人类因素,组织行为和伦理学中获得的综合见解.
- 提出了一个基于集体智能的框架,专注于推理,记忆和注意力.
- 检查了社会技术因素 (团队组成,信任,心理模型,培训,任务结构) 并概述了设计原则以实现互补性.
主要成果:
- 确定了影响人类-人工智能团队有效性的关键社会技术因素.
- 概述了可操作的设计原则,以实现互补性,包括角色划分和持续评估.
- 强调了透明度,信任和以人为中心的设计在人工智能协作中的重要性.
结论:
- 人与人工智能的互补性可以通过小心地组织团队和任务来实现.
- 有效的人类-人工智能团队需要注意认知过程,社会技术因素和道德考虑.
- 拟议的框架和原则为开发高性能,适应性,透明性和可信度的人类-人工智能系统提供了路线图,这些系统与人类价值观保持一致.
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