A New Perspective On AI Safety Through Control Theory Methodologies

TL;DR

This paper presents a novel interdisciplinary approach to AI safety by applying control theory principles, aiming to enhance safety assurance in autonomous, safety-critical AI systems through a systematic, data-driven control perspective.

Contribution

It introduces the concept of data control, integrating control theory with AI safety, and provides a generic, abstract foundation for safety analysis adaptable to various AI systems.

Findings

Proposes a control theory-inspired framework for AI safety

Establishes a top-down safety analysis methodology

Prepares a foundation for future safety innovations in AI

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.