Decentralized AI Governance in Blockchain Ecosystems: A Federated Learning and Oracle-Based Framework

This article presents a comprehensive governance framework that integrates Artificial Intelligence (AI) decision-making into Decentralized Autonomous Organizations (DAOs) operating on blockchain networks. The proposed architecture leverages federated learning for decentralized data processing and introduces AI oracle contracts to deliver transparent, auditable governance recommendations on-chain. A Hybrid Human-AI Voting Mechanism is designed to ensure community oversight while enabling scalable, data-driven governance decisions. The paper explores practical applications of this model in DAO treasury management, demonstrating how AI-assisted governance can reduce decision-making cycles and enhance analytical rigor. Key challenges such as algorithmic bias, Sybil resistance in federated nodes, and the explainability of AI decisions are addressed through strategic mitigations, including zero-knowledge proofs and community-centric audit mechanisms. This framework contributes to the evolving landscape of autonomous, transparent, and scalable governance models in the blockchain ecosystem.