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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.14519 |
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| _version_ | 1866915338638065664 |
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| author | Balan, Kar Gilbert, Andrew Collomosse, John |
| author_facet | Balan, Kar Gilbert, Andrew Collomosse, John |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14519 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Content ARCs: Decentralized Content Rights in the Age of Generative AI Balan, Kar Gilbert, Andrew Collomosse, John Computers and Society Artificial Intelligence Digital Libraries Image and Video Processing 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. |
| title | Content ARCs: Decentralized Content Rights in the Age of Generative AI |
| topic | Computers and Society Artificial Intelligence Digital Libraries Image and Video Processing |
| url | https://arxiv.org/abs/2503.14519 |