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IDs for AI Systems
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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 10 Pith papers
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Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms
VLMs preserve linearly separable visual magnitudes and can compare them, yet collapse at symbolic mapping because visual and textual number spaces remain fractured and disjoint.
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Attacks and Mitigations for Distributed Governance of Agentic AI under Byzantine Adversaries
Identifies concrete attacks from a malicious Provider on SAGA and proposes SAGA-BFT, SAGA-MON, SAGA-AUD, and SAGA-HYB mitigations offering different security-performance trade-offs.
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Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms
LM agents' changeable modules prevent persistent identity and sanction sensitivity, making reputation mechanisms structurally inapplicable and requiring protocol-based behavioral harnesses instead.
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MAGIQ: A Post-Quantum Multi-Agentic AI Governance System with Provable Security
MAGIQ introduces a post-quantum secure system for policy definition, enforcement, and accountability in multi-agent AI using novel cryptographic protocols and UC framework proofs.
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The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems
The 2025 AI Agent Index catalogs technical and safety details for 30 deployed AI agents and finds low developer transparency on safety, evaluations, and societal impacts.
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MAGIQ: A Post-Quantum Multi-Agentic AI Governance System with Provable Security
MAGIQ introduces a post-quantum governance system for multi-agent AI that supports policy budgets, session enforcement, message attribution, and UC-based security proofs while comparing overhead to SAGA.
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Distributed General-Purpose Agent Networks: Architecture, Key Mechanisms, and Prototypes
Proposes layered architecture and three mechanisms (semantic propagation, identity/reputation, semantic-gradient design) for distributed agent networks with prototype simulations.
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AI Identification: An Integrated Framework for Sustainable Governance in Digital Enterprises
The paper introduces a dual-layer AI identification framework that integrates cryptographic, blockchain, and zero-knowledge techniques with governance checkpoints to support lifecycle accountability in digital enterprises.
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LLM Agents Are the Antidote to Walled Gardens
LLM agents enable universal interoperability by serving as automatic translators and adapters between proprietary digital services.
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The Agentic Web Requires New Normative Infrastructure
The agentic web requires new normative infrastructure of laws, norms, and practices to allow user-delegated AI agents to access online properties without being blocked as malicious bots.
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