Content ARCs: Decentralized Content Rights in the Age of Generative AI
This addresses the issue of fair compensation and attribution for creators in the age of generative AI, but it is incremental as it builds on existing works in data licensing.
The paper tackles the problem of balancing creative rights and AI development by proposing Content ARCs, a framework for managing rights and compensating creators when their work is used in AI training, though no concrete numbers are provided.
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.