Summary
- Decentralized AI is framed as an infrastructure choice: distribute data custody and inference to reduce single‑point failures and enable auditability where public impact is high.
- Safety and governance are first‑class requirements. Provenance of data and models, red‑teaming, and policy‑aware inference are necessary when AI informs benefits, licensing, or grid operations.
- Identity links to AI responsibly: VC‑based access control and ZK proofs allow personalized services without unnecessary data exposure.
- Implementation capacity grows via testbeds and sandboxes where academia, startups, and agencies co‑develop standards and evaluate tradeoffs.
- International forums (IEEE, LF) provide neutral venues to align specs and reference implementations for public sector adoption.
Related sessions
- — Decentralized AI
- — Techno‑legal guardrails
- — Communities in conflicts YouTube
- — Startup sandbox
- — Wallets and identity
- — AI and Blockchain
- — Educators and students
Suggested “HMW” prompts
- HMW implement auditable model and data provenance for high‑stakes public decisions?
- HMW structure a national AI x Web3 sandbox with testbeds for identity‑aware services?
- HMW build decentralized inference pilots where trust and robustness matter most?
- HMW standardize red‑teaming and incident response for models used in public services?