AI 中文总结
本文提出人机协作风险的生命周期分类框架，识别六大跨领域风险集群，构建交互模型揭示风险级联机制，为设计更具弹性的人机协作系统提供基础。
AI 中文摘要
随着AI系统日益融入医疗、新闻、教育、科研、组织决策及国防等关键领域，高效人机协作已成为核心挑战。然而，破坏协作的社会技术风险常被孤立研究，掩盖了跨领域的重复故障机制。本文提出一种面向生命周期的人机协作风险综合框架，涵盖任务分配、交互、反馈与采用四个阶段。结合多领域应用证据，识别出六大跨领域重复风险集群：信任校准偏差、认知负荷、责任缺口、能力侵蚀、目标错位及AI焦虑与技术压力。进一步提出概念交互模型，阐释这些风险如何源于社会技术驱动因素、通过级联路径相互作用，最终影响团队绩效与人类福祉。分析表明，诸多协作故障并非源于孤立技术缺陷，而是源于相互关联的社会技术动态，这解释了零散干预为何常产生意外后果。通过将零散文献综合为统一框架，本研究为未来实证研究、面向生命周期的治理及设计更具弹性、可信且以人为本的人机协作系统奠定了基础。
英文摘要
As AI systems become increasingly integrated into consequential domains such as healthcare, journalism, education, scientific research, organizational decision-making, and defense, effective human-AI collaboration has emerged as a critical challenge. However, the sociotechnical risks that undermine collaboration are often studied in isolation, obscuring the recurring failure mechanisms that cut across domains. This paper presents a lifecycle-oriented synthesis of human-AI collaboration risks spanning four stages: task allocation, interaction, feedback, and adoption. Drawing on evidence from diverse application domains, we identify six recurring cross-domain risk clusters: Trust Miscalibration, Cognitive Burden, Accountability Gap, Capability Erosion, Goal Misalignment, and AI Anxiety and Technostress. We further propose a conceptual interaction model that illustrates how these risks emerge from sociotechnical drivers, interact through cascading pathways, and ultimately affect team performance and human well-being. Our analysis shows that many collaboration failures stem not from isolated technical deficiencies but from interconnected sociotechnical dynamics, helping explain why piecemeal interventions frequently create unintended consequences. By synthesizing fragmented literature into a unified framework, this work provides a foundation for future empirical research, lifecycle-oriented governance, and the design of more resilient, trustworthy, and human-centered human-AI collaboration systems.
Comments11 pages, 2 Figures
