  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.
