レジリエンスと自律性の融合：重要インフラにおける具現化AIの統治
Resilience Meets Autonomy: Governing Embodied AI in Critical Infrastructure
重要インフラで使われる具現化AIのレジリエンスを高めるため、人間の判断と機械の能力を組み合わせたハイブリッド統治アーキテクチャを提案し、4つの監視モードを分野ごとに割り当てる枠組みを示した論文。
著者: Puneet Sharma, Christer Henrik Pursiainen
分類: cs.AI, cs.RO
原文アブストラクト
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.