We propose a novel approach to human interaction with artificial intelligence systems (HAII), alternative to the mainstream dyadic one where humans and AI are seen as interacting agents. Through two quantitative experiments and two qualitative in-field case studies, we show that the mainstream HAII paradigm presents potentially harmful design shortcomings as it can trigger negative dynamics such as automation bias and prejudices. Our proposal, on the other hand, is grounded in the Computer-Supported Cooperative Work literature, in which AI can be conceived as a component of a Knowledge Artifact (KA). This consists of an ecosystem of knowledge creation tools whose goal is to support a Ba (after Nonaka), i.e. a collective of competent decision makers. We highlight the cooperative nature of decision making and the AI functionalities that a KA should embed. These include eXplainable AI solutions, aimed at facilitating appropriation, but also functionalities that enable reasoning in a collaborative setting. Finally, we discuss how moving intelligence and agency from individual agents to the human collective can help to mitigate the shortcomings of dyadic HAII (e.g., deskilling), re-distribute responsibility in critical tasks, and revisit the HAII research agenda to align it with the needs of increasingly wide, heterogeneous and complex teams.

我们提出了一种人类与人工智能系统 (HAII) 交互的新方法，替代了将人类和人工智能视为交互代理的主流二元方法。通过两个定量实验和两个定性现场案例研究，我们表明主流 HAII 范式存在潜在的有害设计缺陷，因为它会引发负面动态，例如自动化偏见和偏见。另一方面，我们的提议基于计算机支持的合作工作文献，其中 AI 可以被视为知识工件 (KA) 的一个组成部分。这包括知识创造工具的生态系统，其目标是支持 Ba（在 Nonaka 之后），即一群有能力的决策者。我们强调决策的合作性质和 KA 应该嵌入的 AI 功能。其中包括旨在促进拨款的可解释人工智能解决方案，以及在协作环境中实现推理的功能。最后，我们讨论了将智能和代理从个体代理转移到人类集体如何有助于减轻二元 HAII（例如，去技能）的缺点，重新分配关键任务的责任，并重新审视 HAII 研究议程以使其与日益广泛、异构和复杂的团队的需求。