
Contents
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Introduction Introduction
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Self-Reporting by AI Models: Current Challenges of Watermarking and Metadata Labeling Self-Reporting by AI Models: Current Challenges of Watermarking and Metadata Labeling
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Forming Chains of Trust: A New Approach to Tackling Synthetic Data Risks Forming Chains of Trust: A New Approach to Tackling Synthetic Data Risks
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Unique Identifiers: What Are They? Unique Identifiers: What Are They?
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Verifiable Digital Credentials: What Are They? Verifiable Digital Credentials: What Are They?
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Potential Role of Distributed Ledger Technology Potential Role of Distributed Ledger Technology
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How the Chains of Trust Model Can Support Content Authenticity How the Chains of Trust Model Can Support Content Authenticity
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Role of Digital Signatures in the Chains of Trust Model Role of Digital Signatures in the Chains of Trust Model
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Using the Chains of Trust Model to Certify Content Authenticity Using the Chains of Trust Model to Certify Content Authenticity
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Current Challenges in the Chains of Trust Model Current Challenges in the Chains of Trust Model
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Governance Challenges Governance Challenges
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Technical Challenges Technical Challenges
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Metadata Challenges Metadata Challenges
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Supporting Policy Recommendations Supporting Policy Recommendations
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The Right to Self-Certify The Right to Self-Certify
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Tech-Agnostic Policies Tech-Agnostic Policies
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Improving Metadata Standards Improving Metadata Standards
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Decentralization, Open Access, and Data Minimization Decentralization, Open Access, and Data Minimization
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Conclusion Conclusion
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Illustration of Digital Credentials in Action Illustration of Digital Credentials in Action
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Political Campaign Use Case Political Campaign Use Case
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Operation of Chains of Trust System Operation of Chains of Trust System
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Health Care Use Case Health Care Use Case
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Operation of Chains of Trust System Operation of Chains of Trust System
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Key Design Principles for Digital Verifiable Credentials Key Design Principles for Digital Verifiable Credentials
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Acknowledgments Acknowledgments
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References References
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Notes Notes
Chains of Trust: Combatting Synthetic Data Risks of AI
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Published:20 March 2025
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Abstract
Generative artificial intelligence (GenAI) represents the proliferation of data, whereas cryptographic technologies represent data scarcity. These two technologies can balance and check each other. Yet, policymakers have been considering GenAI risks in a silo without considering the power of cryptographic proofing and distributed ledger technologies. Accompanying the extraordinary benefits of GenAI are also significant new risks stemming from AI deepfakes and other types of AI-powered fraud. This article does not judge synthetic data generation, which can have important economic benefits. Nonetheless, it is difficult to distinguish synthetic data from authentic data not produced by AI. This article proposes a “chains of trust” model, which empowers authentic content producers and consumers with the cryptographic tools to certify, verify, and, therefore, distinguish authentic content from AI-generated synthetic content, which in turn helps establish trust in the provenance of both synthetic and authentic data. Then it examines the limitations of current approaches to mitigating GenAI risks—primarily watermarking and traditional metadata methods that rely on self-reporting—but also how they can be utilized for self-certification by natural and legal persons of their authentic data. The chains of trust model aims to be an overarching conceptual framework for data provenance and authenticity that can help unify and extend current efforts, such as the Coalition for Content Provenance and Authenticity standard and personhood credentials. This article also recommends a new personal data right—the right to self-certify one’s authentic data. Ultimately, it aims to introduce concepts and common terminology that can be useful in the important policy discourse on regulating AI risks without restricting innovation.
Steinhardt School of Culture, Education, and Human Development
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