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
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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.

We also analyse and discuss how existing decentralized content licensing implementations map onto the Content ARCs framework and summarize most relevant systems in this table. A few patterns stand out: NFT-based systems focus on ownership and Rights, but they don’t support attribution or downstream Compensation. EKILA’s ORA framework, which is an early paper of mine, comes closest to implementing the full ARC stack: it uses C2PA for provenance, NFTs for rights, and micropayments for AI reuse based on visual attribution, but it still lacks a standardized way to represent rights. Ocean Protocol and Story Protocol lean into data tokenization and licensing via smart contracts, but skip verification of provenance and attribution. Tools like JPEG Trust and Fox Verify support Authenticity quite well, but they largely leave licensing and compensation out of scope. Commercial platforms like Bria, ProRata, and Getty focus Compensation, but offer little transparency on their compensation models for creatives One key takeaway: no system today fully implements all three ARC phases in a robust and standardised way. This fragmentation highlights the need for common standards—particularly around Rights and Compensation, where current practice is still ad hoc or proprietary.

Presentation

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",
      }