Request for Feedback: A Technical Policy Blueprint for Trustworthy Decentralized AI
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Alex
Jan 2, 2026, 12:45:07 PMJan 2
to Machine Learning News
Dear All,
We invite the ML community to contribute to the collaborative community effort for technical policies that aim to help unlock governance in decentralized AI settings. Specifically we welcome:
About the Technical Policy Blueprint
The majority of the world's data is stored in private settings. Decentralized AI (e.g. Federated Learning) can unlock AI development on such private data without sharing. However strong governance (e.g. data access/use controls, auditability, etc) is required to further unlock decentralized AI (e.g. scalability, easiness, protection, etc.). Our proposed collaborative blueprint introduces:
→ Policy-as-code objects: Community-driven, machine-readable templates that encode AI governance requirements transparently
→ A Policy Engine that verifies evidence (e.g., signatures, credentials, payment proofs, etc.) and issues capability packages
→ Asset Guardians that simply verify and apply these packages—no reconfiguration needed when policies change.
The key insight: Don't make every system understand every policy. Instead, create a Policy Engine that checks the evidence and issues a "capability package." Asset Guardians then just verify that package and grant access to digital assets.
This decoupling aims to make decentralized AI systems even more transparent, auditable, interoperable and resilient to change.
About the Working Group
We are researchers and engineers from major technology companies and research institutions and we initiated "Technical Policy Blueprint for Trustworthy Decentralized AI" as an open collaborative community effort that aims to help with digital asset access/use through technical policies in decentralized settings.
About MLCommons
MLCommons (https://mlcommons.org/) is a non-profit consortium that aims to accelerate the benefits of machine learning and artificial intelligence. Our members and partners include over 125 organizations from around the world, many of which are leading technology companies, startups, academics, and nonprofits that are actively researching, developing, and deploying artificial intelligence products for customers. Critically, our founding membership includes academic researchers at the forefront of machine learning research, and the research community continues to be core to our membership helping to lead many of our working groups. MLCommons acts as a neutral nexus for commercial and non-commercial actors to collaborate on tools that advance the field.
Sincerely,
- Feedback to our inaugural paper "Technical Policy Blueprint for Trustworthy Decentralized AI" (https://arxiv.org/abs/2512.11878)
- Contribute use cases that can be further improved and enabled via technical policies
About the Technical Policy Blueprint
The majority of the world's data is stored in private settings. Decentralized AI (e.g. Federated Learning) can unlock AI development on such private data without sharing. However strong governance (e.g. data access/use controls, auditability, etc) is required to further unlock decentralized AI (e.g. scalability, easiness, protection, etc.). Our proposed collaborative blueprint introduces:
→ Policy-as-code objects: Community-driven, machine-readable templates that encode AI governance requirements transparently
→ A Policy Engine that verifies evidence (e.g., signatures, credentials, payment proofs, etc.) and issues capability packages
→ Asset Guardians that simply verify and apply these packages—no reconfiguration needed when policies change.
The key insight: Don't make every system understand every policy. Instead, create a Policy Engine that checks the evidence and issues a "capability package." Asset Guardians then just verify that package and grant access to digital assets.
This decoupling aims to make decentralized AI systems even more transparent, auditable, interoperable and resilient to change.
About the Working Group
We are researchers and engineers from major technology companies and research institutions and we initiated "Technical Policy Blueprint for Trustworthy Decentralized AI" as an open collaborative community effort that aims to help with digital asset access/use through technical policies in decentralized settings.
MLCommons (https://mlcommons.org/) is a non-profit consortium that aims to accelerate the benefits of machine learning and artificial intelligence. Our members and partners include over 125 organizations from around the world, many of which are leading technology companies, startups, academics, and nonprofits that are actively researching, developing, and deploying artificial intelligence products for customers. Critically, our founding membership includes academic researchers at the forefront of machine learning research, and the research community continues to be core to our membership helping to lead many of our working groups. MLCommons acts as a neutral nexus for commercial and non-commercial actors to collaborate on tools that advance the field.
Sincerely,
Alex Karargyris, PhD
Co-Chair for Medical WG
MLCommons
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