Resilience Meets Autonomy: Governing Embodied AI in Critical Infrastructure
This addresses the problem of AI reliability in high-stakes infrastructure for policymakers and engineers, but it is incremental as it builds on existing governance frameworks.
The paper tackles the challenge of ensuring resilience in embodied AI systems used in critical infrastructure, which face cascading failures beyond their training assumptions, by proposing a hybrid governance architecture that balances autonomy with oversight modes tailored to sector-specific risks.
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.