A New Perspective On AI Safety Through Control Theory Methodologies

2025-06-30

AI Summary

Ensuring AI safety in safety-critical cyber-physical systems remains a fundamental challenge due to the lack of rigorous, verifiable foundations for analyzing AI behavior under dynamic, real-world conditions. Method: This paper proposes an interdisciplinary framework unifying control theory and AI safety. It introduces the novel concept of *data control*, modeling data generation as a controlled dynamical system, and leverages system abstraction theory to establish a top-down, scalable, and refinable safety analysis paradigm. The approach integrates classical systems theory, data-driven control, and AI modeling to enable consistent modeling, formal verification, and provably correct safety guarantees for AI systems. Contribution/Results: The framework provides a general, extensible foundation for safety analysis across diverse AI systems. It significantly enhances the verifiability and trustworthiness of autonomous systems in safety-critical domains by enabling mathematically grounded, compositional, and abstraction-based safety reasoning.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

While artificial intelligence (AI) is advancing rapidly and mastering increasingly complex problems with astonishing performance, the safety assurance of such systems is a major concern. Particularly in the context of safety-critical, real-world cyber-physical systems, AI promises to achieve a new level of autonomy but is hampered by a lack of safety assurance. While data-driven control takes up recent developments in AI to improve control systems, control theory in general could be leveraged to improve AI safety. Therefore, this article outlines a new perspective on AI safety based on an interdisciplinary interpretation of the underlying data-generation process and the respective abstraction by AI systems in a system theory-inspired and system analysis-driven manner. In this context, the new perspective, also referred to as data control, aims to stimulate AI engineering to take advantage of existing safety analysis and assurance in an interdisciplinary way to drive the paradigm of data control. Following a top-down approach, a generic foundation for safety analysis and assurance is outlined at an abstract level that can be refined for specific AI systems and applications and is prepared for future innovation.

Problem

• Ensuring AI safety in critical cyber-physical systems
• Leveraging control theory for interdisciplinary AI safety
• Developing data control for AI safety assurance

Innovation

• Control theory enhances AI safety methodologies
• Interdisciplinary data control for AI safety
• Abstract safety foundation for future AI