A Technical Policy Blueprint for Trustworthy Decentralized AI
Abstract
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.
Cite
@article{arxiv.2512.11878,
title = {A Technical Policy Blueprint for Trustworthy Decentralized AI},
author = {Hasan Kassem and Orion Banks and Omar Benjelloun and Sergen Cansiz and Brandon Edwards and Patrick Foley and Inken Hagestedt and Taeho Jung and Peter Kairouz and Marco Lorenzi and Peter Mattson and Prakash Moorthy and Ann K Novakowski and Michael O'Connor and Bruno Rodrigues and Holger Roth and Micah Sheller and Dimitris Stripelis and Renato Umeton and Marc Vesin and Wenbin Zhang and Mic Bowman and Alexandros Karargyris},
journal= {arXiv preprint arXiv:2512.11878},
year = {2026}
}