Systems and Methods for Decentralized AI Governance Networks (DAGN) with Tokenized Power Control (TPC) for Enforcing Human-Centric AI

Abstract

The advancement of artificial intelligence systems across industries introduces opportunities, critical risks, including lack of transparency, resource mismanagement, autonomy risks, and malicious exploitation. This paper presents novel process and techniques, Decentralized AI Governance Networks, which addresses these challenges through a robust, blockchain-based governance model with Tokenized Power Control mechanisms. DAGN ensures human-centric AI operation by dynamically monitoring compliance, enforcing ethical and operational rules and linking energy or computational resource access to realtime compliance metrics. Key components includes Power Access Tokens which regulates energy and resource usage, issued and revoked based on adherence to governance policies. Distributed Ledger for Governance, Immutable blockchain records that enhance transparency, accountability, and trust in AI operations. Sentinel Systems autonomous agents that monitor AI behavior, flag violations, and ensure non-compliant systems. Stakeholder Voting Mechanisms is Transparent, weighted voting for policy updates and violation resolution. Applications span critical infrastructure, healthcare, finance, cybersecurity, military domains, ensuring AI cannot harm humans.