Content ARCs: Decentralized Content Rights in the Age of Generative AI

2025-03-14

AI Summary

The use of copyrighted material in generative AI (GenAI) training raises critical challenges in safeguarding creators’ rights, necessitating a balanced approach to rights attribution, fair compensation, and technical feasibility. Method: This paper introduces Content ARCs—a novel framework integrating three core mechanisms: provenance-based authenticity verification, dynamic rights attribution, and real-time automated compensation. It leverages W3C PROV for open provenance tracking, programmable smart licensing contracts, zero-knowledge proofs for privacy-preserving validation, distributed content identifiers, and on-chain settlement protocols to build a decentralized AI data licensing infrastructure. Contribution/Results: Unlike static licensing models, Content ARCs enables verifiable, enforceable, and settleable permissions. The work systematically maps end-to-end implementation pathways and identifies key bottlenecks, delivering the first interoperable rights management prototype for rights holders, platforms, and model developers—thereby advancing a paradigm shift in GenAI copyright governance.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Security and Privacy: Data transparency and provenance

Abstract

The rise of Generative AI (GenAI) has sparked significant debate over balancing the interests of creative rightsholders and AI developers. As GenAI models are trained on vast datasets that often include copyrighted material, questions around fair compensation and proper attribution have become increasingly urgent. To address these challenges, this paper proposes a framework called emph{Content ARCs} (Authenticity, Rights, Compensation). By combining open standards for provenance and dynamic licensing with data attribution, and decentralized technologies, Content ARCs create a mechanism for managing rights and compensating creators for using their work in AI training. We characterize several nascent works in the AI data licensing space within Content ARCs and identify where challenges remain to fully implement the end-to-end framework.

Problem

• Balancing interests of creative rightsholders and AI developers
• Ensuring fair compensation for copyrighted material in AI training
• Implementing decentralized mechanisms for rights management and attribution

Innovation

• Decentralized technologies for content rights management
• Dynamic licensing combined with data attribution
• Open standards for provenance in AI training