AI systems are increasingly pervasive, yet information needed to decide
whether and how to engage with them may not exist or be accessible. A user may
not be able to verify whether a system satisfies certain safety standards. An
investigator may not know whom to investigate when a system causes an incident.
A platform may find it difficult to penalize repeated negative interactions
with the same system. Across a number of domains, IDs address analogous
problems by identifying \textit{particular} entities (e.g., a particular Boeing
747) and providing information about other entities of the same class (e.g.,
some or all Boeing 747s). We propose a framework in which IDs are ascribed to
\textbf{instances} of AI systems (e.g., a particular chat session with Claude
3), and associated information is accessible to parties seeking to interact
with that system. We characterize IDs for AI systems, argue that there could be
significant demand for IDs from key actors, analyze how those actors could
incentivize ID adoption, explore potential implementations of our framework,
and highlight limitations and risks. IDs seem most warranted in high-stakes
settings, where certain actors (e.g., those that enable AI systems to make
financial transactions) could experiment with incentives for ID use. Deployers
of AI systems could experiment with developing ID implementations. With further
study, IDs could help to manage a world where AI systems pervade society.

提出一个框架，在其中为 AI 系统的实例分配标识，并为寻求与该系统交互的各方提供相关信息。讨论了 AI 系统的标识、潜在需求、激励机制、实现方式以及限制和风险，并指出在高风险场景下更有必要使用标识。通过进一步研究，标识可以帮助管理人工智能系统普遍渗透于社会的世界。