Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage

2024-04-09

arXiv.org

AI Summary

To address static copyright management, poor provenance tracking, and delayed license updates in AI model training, this paper proposes IBis—a blockchain-based framework introducing the first on-chain/off-chain coordination mechanism tailored to the dynamic training trajectory of AI models. IBis leverages Daml smart contracts deployed on the Canton platform to enable on-chain registration, full-lifecycle provenance tracing, and fine-grained dynamic license management for datasets, models, and associated permissions. It supports verifiable retraining and automated license renewal in multi-stakeholder settings. Unlike static NFT-based copyright solutions, IBis achieves low latency and high throughput under realistic workloads (hundreds of users, thousands of assets), demonstrating industrial-grade feasibility and horizontal scalability. As the first infrastructure for decentralized AI training, IBis enables real-time compliance verification and iterative license evolution.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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 extsc{IBis}, a blockchain-based framework tailored for AI model training workflows. extsc{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. extsc{IBis} enables iterative model retraining and fine-tuning, and offers flexible license checks and renewals. Further, extsc{IBis} provides APIs designed for seamless integration with existing contract management software, minimizing disruptions to established model training processes. We implement extsc{IBis} using Daml on the Canton blockchain. Evaluation results showcase the feasibility and scalability of extsc{IBis} across varying numbers of users, datasets, models, and licenses.

Problem

• Ensuring AI ownership and ethical use in content generation
• Dynamic copyright management in AI training workflows
• Decentralized compliance for data provenance and licensing

Innovation

• Blockchain-based framework for AI training workflows
• Dynamic copyright and provenance management system
• On-chain registries with off-chain signing services