Towards Trustworthy Physical Intelligence: From Theory to Practice Across Life Cycle

Physical AI systems require fundamentally different safety approaches than digital AI because their actions have irreversible physical consequences in the real world—a robot making a mistake can cause injury or damage that cannot be undone like deleting a digital file, making physical safety a critical new concern for trustworthy AI governance.

Existing trustworthy AI frameworks are insufficient for physical intelligence systems because they were designed for digital-only applications and don't adequately address distinctive challenges like cyber-physical security (where hackers could compromise both software and physical hardware) and manufacturing process integrity.

The paper introduces a comprehensive life cycle approach (T-PAIO) for governing physical AI systems across five core stages of development and deployment, suggesting that trustworthiness must be built in from initial design through manufacturing, deployment, and ongoing operation—not added as an afterthought.

Physics itself must be integrated into AI trustworthiness frameworks rather than treating physical systems as black boxes, meaning developers need to understand and verify that AI systems respect real-world physical laws, dynamics, and constraints to prevent dangerous or impossible behaviors.

A new theoretical framework (T-PAI) is needed to organize trustworthiness principles specifically for physical intelligence, establishing common standards and governance approaches for robots, autonomous vehicles, and other systems that interact with the physical world in ways that could affect human safety.