为复杂环境设计人类-人工智能系统:来自社会技术系统的分布式,联合和自我组织视角的想法和认知工作分析
Neelam Naikar1, Ashleigh Brady1, Glennn Moy1
1Defence Science and Technology Group, Australia.
Ergonomics
|November 29, 2023
概括
当前的人机交互模型对于复杂的操作来说太简单了. 社会技术系统为在现实环境中设计有效的人类-人工智能合作提供了更好的框架.
科学领域:
- 人与计算机的互动.
- 认知科学是一种认知科学.
- 社会技术系统 社会技术系统
背景情况:
- 复杂的操作设置涉及多个分布式团队,文物和技术,正如COVID-19流行病和野火等事件所强调的那样.
- 现有的人机交互模型主要是双向的,专注于单个人机交互,这对于复杂的多代理环境来说是不够的.
- 新兴的人工智能 (AI) 技术为在复杂领域运营的组织带来了新的机遇和挑战.
研究的目的:
- 提议从双向模型向社会技术系统的转变,为设计人类-人工智能交互提供视角.
- 探索分布式认知,联合认知系统和自我组织的概念如何为人类-人工智能系统设计提供信息.
- 倡导基于对复杂工作绩效的当代理解的设计框架.
主要方法:
- 社会技术系统理论的审查和综合.
- 将分布式认知,联合认知系统和自我组织等概念应用于人类-人工智能系统.
- 讨论认知工作分析作为一个相关的设计框架.
主要成果:
- 社会技术系统的观点为理解和设计复杂的操作环境中的人与人工智能的交互提供了更合适的框架.
- 分布认知,联合认知系统和自我组织的原则为创建有效的人类-人工智能系统提供了可操作的见解.
- 认知工作分析是复杂的工作表现的设计方法的一个例子.
结论:
- 未来的人类-人工智能系统设计必须超越双向模型,拥抱社会技术视角.
- 利用分布式认知和自我组织的概念对于开发强大的人类-人工智能系统至关重要.
- 考虑复杂工作绩效的整体设计框架对于成功的AI集成至关重要.
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