Research and application have used human-AI teaming (HAT) as a new paradigm to develop AI systems. HAT recognizes that AI will function as a teammate instead of simply a tool in collaboration with humans. Effective human-AI teams need to be capable of taking advantage of the unique abilities of both humans and AI while overcoming the known challenges and limitations of each member, augmenting human capabilities, and raising joint performance beyond that of either entity. The National AI Research and Strategic Plan 2023 update has recognized that research programs focusing primarily on the independent performance of AI systems generally fail to consider the functionality that AI must provide within the context of dynamic, adaptive, and collaborative teams and calls for further research on human-AI teaming and collaboration. However, there has been debate about whether AI can work as a teammate with humans. The primary concern is that adopting the "teaming" paradigm contradicts the human-centered AI (HCAI) approach, resulting in humans losing control of AI systems. This article further analyzes the HAT paradigm and the debates. Specifically, we elaborate on our proposed conceptual framework of human-AI joint cognitive systems (HAIJCS) and apply it to represent HAT under the HCAI umbrella. We believe that HAIJCS may help adopt HAI while enabling HCAI. The implications and future work for HAIJCS are also discussed. Insights: AI has led to the emergence of a new form of human-machine relationship: human-AI teaming (HAT), a paradigmatic shift in human-AI systems; We must follow a human-centered AI (HCAI) approach when applying HAT as a new design paradigm; We propose a conceptual framework of human-AI joint cognitive systems (HAIJCS) to represent and implement HAT for developing effective human-AI teaming

翻译：研究与实践已将人机协同（HAT）作为开发人工智能系统的新范式。HAT 认为，人工智能将作为团队成员而非单纯工具与人类协作。高效的人机团队需要能够充分利用人类与人工智能的独特能力，同时克服彼此已知的挑战与局限，增强人类能力，并提升整体绩效至超越任一单方水平。2023年《国家人工智能研究与战略规划》更新指出，主要聚焦于人工智能系统独立性能的研究项目通常未能考虑其在动态、自适应且具有协作性的团队中应提供的功能，并呼吁进一步研究人机协同与合作。然而，关于人工智能能否作为团队成员与人类协作一直存在争议。主要关切在于采用“协同”范式与以人为本的人工智能（HCAI）方法相悖，可能导致人类失去对人工智能系统的控制。本文进一步分析了 HAT 范式及相关争论。具体而言，我们详细阐述了所提出的人机联合认知系统（HAIJCS）概念框架，并运用其在 HCAI 框架下表征 HAT。我们认为 HAIJCS 有助于在采纳 HAI 的同时实现 HCAI。此外，还讨论了 HAIJCS 的启示与未来工作。见解：人工智能催生了人机关系的新形式——人机协同（HAT），这是人机系统的范式转变；在将 HAT 作为新设计范式应用时，必须遵循以人为本的人工智能（HCAI）方法；我们提出人机联合认知系统（HAIJCS）概念框架，以表征与实现 HAT，从而开发高效的人机协同。