Decentralized AI and the Future of AI Rights Management

Abstract

As artificial intelligence (AI) systems scale across sectors, the regimes governing who may access, use, audit, license, and benefit from models, weights, datasets, prompts, and outputs remain fragmented. “AI rights management” (AIRM) refers to the technical, legal, and institutional mechanisms that allocate these rights and duties across creators, data contributors, model publishers, deployers, and end-users. Centralized approaches—terms of service enforced by platforms, siloed registries, and proprietary compliance APIs—struggle to provide transparency, composability, and cross-jurisdictional enforceability, especially when models and content are remixed across organizational and national boundaries. This manuscript argues that decentralized identity, verifiable provenance, content authenticity, and programmable licensing—coordinated by ledgers and decentralized storage—offer a credible path to future-proof AIRM. We synthesize insights from emerging regulations (e.g., the EU AI Act, the EU Data Act), public-law guidance (e.g., U.S. Copyright Office positions on human authorship), standards (W3C Verifiable Credentials, Decentralized Identifiers, PROV), and industry efforts (C2PA, IPFS, Solid pods, open and responsible AI licensing). Building on this base, we propose a layered architecture: (1) decentralized identity and role binding; (2) machine-verifiable rights assertions and provenance; (3) tokenized or programmable licensing and revenue distribution; (4) auditability and dispute resolution through cryptographic attestations and governed data trusts. Methodologically, we employ a design-science synthesis with normative legal analysis and scenario-based evaluation in creative media and dataset contribution contexts. The results show how decentralized primitives can make AI rights legible and enforceable ex ante (through embedding rights in artifacts and workflows) rather than ex post policing. We conclude with a scope and limitations section that surfaces governance pitfalls (e.g., sybil risk and unequal bargaining power), regulatory alignment challenges, and open research questions on watermark robustness, cross-chain portability, and human-centric remedies.