Resilience Meets Autonomy: Governing Embodied AI in Critical Infrastructure

TL;DR

This paper explores how to govern embodied AI in critical infrastructure by balancing autonomy and oversight to enhance resilience against complex crisis dynamics.

Contribution

It introduces a hybrid governance framework with four oversight modes tailored to infrastructure sectors, integrating standards and crisis management insights.

Findings

Four oversight modes mapped to infrastructure sectors

Structured allocation of machine and human roles proposed

Governance enhances resilience against complex failures

Abstract

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.