A Technical Policy Blueprint for Trustworthy Decentralized AI
For developers and operators of decentralized AI systems (e.g., federated learning marketplaces), this work addresses the challenge of achieving scalable and interoperable governance, though it is an incremental architectural proposal without empirical validation.
The paper proposes a Technical Policy Blueprint for decentralized AI that separates policy verification from enforcement using policy-as-code objects and capability packages, aiming to improve interoperability and trust. The approach is designed to be transparent, auditable, and resilient to change, but no concrete performance numbers are provided.
Decentralized AI systems, such as federated learning, can play a critical role in further unlocking AI asset marketplaces (e.g., healthcare data marketplaces) thanks to increased asset privacy protection. Unlocking this big potential necessitates governance mechanisms that are transparent, scalable, and verifiable. However current governance approaches rely on bespoke, infrastructure-specific policies that hinder asset interoperability and trust among systems. We are proposing a Technical Policy Blueprint that encodes governance requirements as policy-as-code objects and separates asset policy verification from asset policy enforcement. In this architecture the Policy Engine verifies evidence (e.g., identities, signatures, payments, trusted-hardware attestations) and issues capability packages. Asset Guardians (e.g. data guardians, model guardians, computation guardians, etc.) enforce access or execution solely based on these capability packages. This core concept of decoupling policy processing from capabilities enables governance to evolve without reconfiguring AI infrastructure, thus creating an approach that is transparent, auditable, and resilient to change.