提出五种人机协作领导力模式，帮领导者看清决策权的真实归属。
Leading Across the Spectrum of Human-AI Relationships: A Conceptual Framework for Increasingly Heterogeneous Teams
- 构建从纯人类到纯AI的五级协作谱系，明确决策主导权位置。
- 指出领导错判权力转移会导致监督形同虚设或干预适得其反。
- 强调团队需具备共同适应能力，适合设计与部署AI系统的管理者。
当人类与人工智能共同参与决策时，什么决定了其重要性？答案正变得模糊：决策看似由人主导，实则由AI设定框架；或表面自动化，实则人类判断仍具决定性。本文提出一个面向领导者的谱系框架，涵盖纯人类、半人半机械（人类主导，AI在环）、平权协作、弥诺陶洛斯（AI主导，人类在环）和纯AI五种模式。该谱系聚焦领导力所在：谁定义问题、谁调整方向、谁对后果负责。五个节点帮助领导者识别决策配置的叠加、漂移或变化。核心风险是误判：当决策主导权已转移，仍维持人类中心叙事。可能误以为监督有效，实则流于形式；或保留人类参与，反而损害决策质量。框架引入‘共适应性’概念，即人与非人参与者协同调整以提升效能，置于异质团队情境中，成员在数量、载体、模型架构、能力、速度、记忆及参与形式上各不相同。目标是实用：帮助战略领导者与系统设计者识别当前配置，察觉其变化，并评估是否匹配特定决策。这些配置将决定组织中权力、责任与信任的分配。未来能否可控且值得居住，取决于领导者能否尽早看清决策实际如何被塑造。
原文摘要 · Abstract (English)
What shapes a consequential decision when human and artificial intelligence work on it together? The answer is becoming harder to see. A decision may look human-led after AI has set the frame, or appear automated while human judgment still carries decisive force. This paper offers a leadership-facing spectrum to see those relationships within a bounded mandate: Pure Human, Centaur (human-dominant, with AI in the loop), Co-equal, Minotaur (AI-dominant, with humans in the loop), and Pure AI. The spectrum asks where leadership work occurs: who frames the problem, who redirects the work, and who can answer for what follows. The five positions are landmarks that help leaders recognize configurations as they layer, drift, or change in a single decision. The central risk is misrecognition: leaders may keep a human-centered story in place after decision-shaping authority has shifted elsewhere. They may believe oversight remains meaningful when it has become ceremonial, or keep humans in the loop when their involvement could make the decision worse. The framework introduces co-adaptability, the capacity of a configuration to improve as human and non-human participants adjust together, and places it within heterogeneous teaming, where participants may vary by number, substrate, model architecture, capability, speed, memory, and form of participation. The aim is practical: to help strategic leaders and those designing or deploying AI systems recognize the configuration at work, notice when it shifts, and judge whether it fits the decision before them. These configurations will shape how power, responsibility, and trust are distributed in organizational life. Whether the futures they help create remain governable and worth inhabiting will depend on leaders who can see, early enough, where and how consequential decisions are actually being shaped.
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