Decentralized Governance of Autonomous AI Agents

TL;DR

The paper addresses the governance of autonomous AI agents by critiquing existing frameworks and proposing ETHOS, a decentralized, Web3-based governance model. It combines philosophical foundations (rationality, ethics, goal alignment) with practical Web3 tools (DAOs, SSI, SBTs, ZKPs, oracles) to create a global AI registry, risk-based regulation, and automated compliance. The framework includes decentralized justice, AI-specific liability concepts, and consumer insurance, aiming for transparent, inclusive oversight while mitigating centralization risks. The work is primarily conceptual, outlining a pathway for empirical validation through pilots and cross-stakeholder collaboration to realize scalable, trustable governance for an AI-driven future.

Abstract

Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework, a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future.