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Personhood credentials: Artificial intelligence and the value of privacy-preserving tools to distinguish who is real online

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arxiv 2408.07892 v4 pith:NQSY74QG submitted 2024-08-15 cs.CY

classification cs.CY
keywords credentialsonlinepersonhoodactorschallengepeopleanonymityincreasing
verification ladder T0 review T1 audit T2 compute T3 formal
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Anonymity is an important principle online. However, malicious actors have long used misleading identities to conduct fraud, spread disinformation, and carry out other deceptive schemes. With the advent of increasingly capable AI, bad actors can amplify the potential scale and effectiveness of their operations, intensifying the challenge of balancing anonymity and trustworthiness online. In this paper, we analyze the value of a new tool to address this challenge: "personhood credentials" (PHCs), digital credentials that empower users to demonstrate that they are real people -- not AIs -- to online services, without disclosing any personal information. Such credentials can be issued by a range of trusted institutions -- governments or otherwise. A PHC system, according to our definition, could be local or global, and does not need to be biometrics-based. Two trends in AI contribute to the urgency of the challenge: AI's increasing indistinguishability from people online (i.e., lifelike content and avatars, agentic activity), and AI's increasing scalability (i.e., cost-effectiveness, accessibility). Drawing on a long history of research into anonymous credentials and "proof-of-personhood" systems, personhood credentials give people a way to signal their trustworthiness on online platforms, and offer service providers new tools for reducing misuse by bad actors. In contrast, existing countermeasures to automated deception -- such as CAPTCHAs -- are inadequate against sophisticated AI, while stringent identity verification solutions are insufficiently private for many use-cases. After surveying the benefits of personhood credentials, we also examine deployment risks and design challenges. We conclude with actionable next steps for policymakers, technologists, and standards bodies to consider in consultation with the public.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 7 citations worldwide. Full citation record

  1. Authenticated Delegation and Authorized AI Agents

    cs.CY 2025-01 conditional novelty 5.0 of 10

    A framework extending OAuth 2.0 and OpenID Connect with agent-ID and delegation tokens so AI agents can act on behalf of verified humans with auditable, limited permissions.

  2. 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.

  3. AI and the Future of Digital Public Squares

    cs.CY 2024-12 unverdicted novelty 3.0 of 10

    A multi-stakeholder agenda argues that LLM-enabled collective dialogue, bridging, moderation, and proof-of-humanity tools can strengthen digital public squares if paired with research and safeguards.

  4. Data-Driven Breakthroughs and Future Directions in AI Infrastructure: A Comprehensive Review

    cs.AI 2025-05 unverdicted novelty 1.0 of 10

    A comprehensive review arguing that data volume and access, not algorithms or compute, have driven AI breakthroughs and will determine the next major advance.

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