Blockchain-based Control Over Data to Train AI Models

Abstract

Blockchain serves as a transformative mechanism for enabling secure, transparent, and privacy-preserving control over data used to train artificial intelligence (AI) models. This paper explores blockchain-enabled frameworks—including data provenance, smart contracts, federated learning integration, Non-Fungible Tokens (NFTs)/DataTokens, and token-based incentive structures—to address data ownership, access governance, contribution compensation, and accountability. We survey platforms such as Ocean Protocol, federated learning with blockchain architectures, and decentralized compute networks. Through analysis of methodologies and case studies across healthcare, IoT, and AI marketplaces, we assess system performance, privacy protection, trust, and regulatory alignment. Our results indicate blockchain facilitates granular data control, immutable provenance, and fair compensation models, yet challenges persist around scalability, incentive fairness, and legal interoperability. We conclude with a roadmap outlining standards, hybrid computations, legal frameworks, and governance models to foster robust "Data-AI-Blockchain" ecosystems.