As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount. AI service providers are expected to prioritize responsibly sourcing training data and obtaining licenses from data owners. However, existing studies primarily center on safeguarding static copyrights, which simply treats metadata/datasets as non-fungible items with transferable/trading capabilities, neglecting the dynamic nature of training procedures that can shape an ongoing trajectory. In this paper, we present \textsc{IBis}, a blockchain-based framework tailored for AI model training workflows. \textsc{IBis} integrates on-chain registries for datasets, licenses and models, alongside off-chain signing services to facilitate collaboration among multiple participants. Our framework addresses concerns regarding data and model provenance and copyright compliance. \textsc{IBis} enables iterative model retraining and fine-tuning, and offers flexible license checks and renewals. Further, \textsc{IBis} provides APIs designed for seamless integration with existing contract management software, minimizing disruptions to established model training processes. We implement \textsc{IBis} using Daml on the Canton blockchain. Evaluation results showcase the feasibility and scalability of \textsc{IBis} across varying numbers of users, datasets, models, and licenses.
View on arXiv