让实体AI在关键设施中更可靠，需限制自主权并建立人机协同监管机制。
Resilience Meets Autonomy: Governing Embodied AI in Critical Infrastructure
- 提出四种监督模式，按任务复杂度与风险等级匹配不同监管方式
- 强调在危机场景下，仅靠训练数据无法应对连锁故障，需人工干预
- 适合关注安全可控的智能系统设计者或政策制定者参考
关键基础设施越来越多地采用具身AI进行监控、预测性维护和决策支持。然而，针对统计可建模不确定性设计的AI系统，在面对超出其训练假设的级联失效与危机动态时表现不足。本文认为，具身AI的韧性取决于在混合治理架构下的有限自主权。我们提出了四种监督模式，并根据任务复杂度、风险水平与后果严重性，将其映射到不同关键基础设施领域。结合欧盟《人工智能法案》、ISO安全标准及危机管理研究，我们主张有效的治理需要对机器能力与人类判断进行结构化分配。
原文摘要 · Abstract (English)
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.
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