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

Kar Balan[1], Andrew Gilbert[1], John Collomosse[1,2]
[1] DECaDE Centre for the Decentralized Digital Economy, University of Surrey, [2] Adobe Research International Conference on AI and the Digital Economy (CADE'25) 2025
Paper

Content Authenticity, Rights, and Compensation (ARCs) framework, illustrating how content provenance, identification, licensing, and creator identity enable downstream value generation. Green arrows indicate systems feeding into content provenance, while blue arrows feed into licensing.

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 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.

BibTeX

@inproceedings{Balan:CADE:2025,
        AUTHOR = Balan, Kar and  Gilbert Andrew and Collomosse, John",
        TITLE = "Content ARCs: Decentralized Content Rights in the Age of Generative AI",
        BOOKTITLE = "International Conference on AI and the Digital Economy (CADE'25)",
        PAGES = "1-10",
        YEAR = "2025",
      }