[论文解读] Unraveling Human-AI Teaming: A Review and Outlook

Bowen Lou, Tian Lu+2 more|ArXiv.org|Apr 8, 2025

Human-Automation Interaction and Safety被引用 3

一句话总结

该论文通过扩展的团队态势感知框架分析人机协作，并提出关于形成、协调、维护与训练四个方面的研究展望，以实现可持续、高效的人机团队。

ABSTRACT

Artificial Intelligence (AI) is advancing at an unprecedented pace, with clear potential to enhance decision-making and productivity. Yet, the collaborative decision-making process between humans and AI remains underdeveloped, often falling short of its transformative possibilities. This paper explores the evolution of AI agents from passive tools to active collaborators in human-AI teams, emphasizing their ability to learn, adapt, and operate autonomously in complex environments. This paradigm shifts challenges traditional team dynamics, requiring new interaction protocols, delegation strategies, and responsibility distribution frameworks. Drawing on Team Situation Awareness (SA) theory, we identify two critical gaps in current human-AI teaming research: the difficulty of aligning AI agents with human values and objectives, and the underutilization of AI's capabilities as genuine team members. Addressing these gaps, we propose a structured research outlook centered on four key aspects of human-AI teaming: formulation, coordination, maintenance, and training. Our framework highlights the importance of shared mental models, trust-building, conflict resolution, and skill adaptation for effective teaming. Furthermore, we discuss the unique challenges posed by varying team compositions, goals, and complexities. This paper provides a foundational agenda for future research and practical design of sustainable, high-performing human-AI teams.

研究动机与目标

• 识别当前人机协作研究在将AI与人类价值观对齐及将AI作为真正团队成员方面的空白。
• 扩展团队态势感知理论以纳入AI队友并定义可操作的研究框架。
• 提出一个四维议程——形成、协调、维护、训练——以推动人机协作。
• 讨论在多样化团队组成与情境中的信任、问责与适应性挑战。

提出的方法

• 综合并扩展团队态势感知（SA）理论，以包含AI角色与动态协调。
• 定义一个扩展的团队SA模型，用于人机协作，强调角色规范与角色流动性。
• 将现有关于人类感知、理解与预测SA的文献映射到AI支持的团队。
• 提供基于团队学习与社会动力学的概念性路线图与未来研究方向。

实验结果

研究问题

• RQ1 如何扩展Team SA以容纳具代理性的AI作为团队成员？
• RQ2 在团队中将AI与人类价值与目标对齐的关键挑战与机制是什么？
• RQ3 形成、协调、维护与训练如何影响人机协作的有效性？
• RQ4 哪些设计原则能提升人机团队中的信任、问责与共享心智模型？

主要发现

• AI代理正在发展为自治、迭代且具有学习与适应能力的团队成员，能够在复杂环境中学习与适应。
• 识别出两个主要空白：将AI与人类价值观对齐，以及将AI能力作为真正的团队成员加以利用。
• 提出一个扩展的Team SA模型，强调共享心智模型、角色规范与角色流动性以支持动态协作。
• 论文概述了一个结构化的四维研究展望：团队形成、协调、维护与训练，旨在解决信任、问责与长期可持续性问题。