韧性与自主性：在关键基础设施中治理具身体验的人工智能
Resilience Meets Autonomy: Governing Embodied AI in Critical Infrastructure
AI总结：
本文探讨关键基础设施中具身体验AI的韧性与自主性治理，提出混合治理架构下的四种监管模式，结合欧盟AI法案和ISO标准，强调机器能力与人类判断的结构化分配。
摘要：
关键基础设施越来越多地采用具身体验AI进行监控、预测性维护和决策支持。然而，设计用于处理可统计表征不确定性的AI系统在面对超出训练假设的连锁故障和危机动态时表现不佳。本文认为，具身体验AI的韧性依赖于混合治理架构中的有限自主性。我们概述了四种监管模式，并根据任务复杂性、风险级别和后果严重性将它们映射到关键基础设施领域。基于欧盟AI法案、ISO安全标准和危机管理研究，我们主张有效的治理需要对机器能力和人类判断进行结构化分配。
英文摘要：
Critical infrastructure increasingly incorporates embodied AI for monitoring, predictive maintenance, and decision support. However, AI systems designed to handle statistically representable uncertainty struggle with cascading failures and crisis dynamics that exceed their training assumptions. This paper argues that Embodied AIs resilience depends on bounded autonomy within a hybrid governance architecture. We outline four oversight modes and map them to critical infrastructure sectors based on task complexity, risk level, and consequence severity. Drawing on the EU AI Act, ISO safety standards, and crisis management research, we argue that effective governance requires a structured allocation of machine capability and human judgement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
