AI Safety vs. AI Security: Demystifying the Distinction and Boundaries
It addresses a foundational problem for AI researchers and policymakers by clarifying terminology to improve collaboration and trust in AI systems, though it is incremental as it builds on existing discourse.
This paper tackles the conceptual confusion between AI Safety and AI Security by providing rigorous definitions and delineating their research boundaries, using analogies to illustrate distinctions and emphasizing their interdependency to guide research and policy.
Artificial Intelligence (AI) is rapidly being integrated into critical systems across various domains, from healthcare to autonomous vehicles. While its integration brings immense benefits, it also introduces significant risks, including those arising from AI misuse. Within the discourse on managing these risks, the terms "AI Safety" and "AI Security" are often used, sometimes interchangeably, resulting in conceptual confusion. This paper aims to demystify the distinction and delineate the precise research boundaries between AI Safety and AI Security. We provide rigorous definitions, outline their respective research focuses, and explore their interdependency, including how security breaches can precipitate safety failures and vice versa. Using clear analogies from message transmission and building construction, we illustrate these distinctions. Clarifying these boundaries is crucial for guiding precise research directions, fostering effective cross-disciplinary collaboration, enhancing policy effectiveness, and ultimately, promoting the deployment of trustworthy AI systems.