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IDs for AI Systems

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arxiv 2406.12137 v3 pith:3ZQNI7SS submitted 2024-06-17 cs.AI

classification cs.AI
keywords systemssysteminformationparticularaccessibleactorsboeingentities
verification ladder T0 review T1 audit T2 compute T3 formal
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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 has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying 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 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, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Agentic Web Requires New Normative Infrastructure

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  2. Steganalysis of Adaptive Covert Collusion in Tool-Using Agent Populations: A Black-Box, Cross-Principal Approach

    cs.CR 2026-08 reject novelty 5.0 of 10

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  3. Private, Verifiable, and Auditable AI Systems

    cs.CR 2025-08 conditional novelty 4.0 of 10

    A thesis demonstrating partial prototypes for zk-verifiable model evaluation and privacy-preserving retrieval, and arguing these pieces can compose into end-to-end auditable AI systems.

  4. A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control

    cs.CR 2025-05 conditional novelty 4.0 of 10

    The authors propose a zero-trust identity and access management framework for AI agents that combines decentralized identifiers, verifiable credentials, a capability-aware naming service, and a global session revocati...

  5. From Turing to Tomorrow: The UK's Approach to AI Regulation

    cs.CY 2025-07 conditional novelty 2.0 of 10

    The UK should establish a flexible, principles-based regulator for frontier AI development, plus defensive measures against biological risks and updated legal frameworks for copyright, discrimination, and AI agents.

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