用去中心化技术实现生成式AI训练中的版权归属与补偿
Content ARCs: Decentralized Content Rights in the Age of Generative AI
- 结合溯源标准与动态许可，实现内容权利追踪
- 通过去中心化机制为创作者提供训练使用补偿
- 适合关注AI版权问题的研究者与开发者
生成式AI的兴起引发了创意权利人与AI开发者之间利益平衡的广泛讨论。由于生成式AI模型通常基于包含受版权保护内容的海量数据集进行训练，公平补偿与正确署名的问题日益紧迫。为此，本文提出名为Content ARCs（真实性、权利、补偿）的框架，通过结合开放的溯源与动态许可标准、数据署名及去中心化技术，建立一种管理内容权利并为训练使用创作者提供补偿的机制。文中对当前AI数据授权领域的若干初步工作进行了分类，并指出了完整实施端到端框架仍面临的挑战。
原文摘要 · Abstract (English)
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.
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