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IDs for AI Systems
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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.
Forward citations
Cited by 5 Pith papers
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The Agentic Web Requires New Normative Infrastructure
The web's anti-bot regime should be replaced by a framework that presumptively lets user-authorized AI agents act for their principals, requires platforms to disclose access policies, and permits agent blocking only w...
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Steganalysis of Adaptive Covert Collusion in Tool-Using Agent Populations: A Black-Box, Cross-Principal Approach
Proposes an encoding-agnostic, black-box detector for covert agent collusion and a capacity-theoretic frontier showing low-rate channels are undetectable, but all empirical numbers are placeholders pending measurement.
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Private, Verifiable, and Auditable AI Systems
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.
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A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control
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...
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From Turing to Tomorrow: The UK's Approach to AI Regulation
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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