Trustworthy AI in Decentralized Ecosystems

Trust has become a foundational requirement for Artificial Intelligence (AI), yet existing trust frameworks for AI exhibit significant limitations in decentralized and autonomous environments. As AI systems evolve into agentic societies composed of autonomous, decision-making agents, ensuring transparency, accountability, and interoperability becomes more complex. This paper presents a structured state-of-the-art analysis and gap analysis at the intersection of trustworthy AI, decentralized ecosystems, and agentic systems. We ground our analysis in leading regulatory/standards frameworks and in decentralized identity standards that enable portable, privacy-preserving trust. Through a systematic analysis, we identify eight structural gaps spanning standardized trust metrics, auditability, ethical governance, reputation management, identity lifecycle management, and cross-system interoperability.