用控制理论新视角提升AI安全，强调数据生成与系统抽象的协同分析。
A New Perspective On AI Safety Through Control Theory Methodologies
- 从系统理论出发，将数据生成过程视为可控制的动态系统。
- 提出'数据控制'概念，为AI安全提供可扩展的抽象分析框架。
- 适合关注AI安全工程、系统可靠性与跨学科方法的研究者。
尽管人工智能在复杂问题上展现出惊人性能，其安全性保障仍是关键挑战，尤其在涉及真实世界网络物理系统的高风险场景中。本文提出一种基于控制理论的新视角，通过系统理论与系统分析驱动的方法，重新诠释人工智能系统中的数据生成机制及其抽象过程。该视角被称为‘数据控制’，旨在推动人工智能工程利用现有安全分析与保障手段，实现跨学科融合。文章采用自上而下的方法，在抽象层面构建通用的安全分析与保障基础，可进一步细化应用于具体AI系统和应用场景，为未来创新预留空间。
原文摘要 · Abstract (English)
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.
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