Abstract
Decentralized Autonomous Organizations (DAOs) represent a promising frontier in organizational governance, but their decision-making processes often face challenges of scalability, efficiency, and quality. This study explores the potential of integrating artificial intelligence systems with human collective intelligence in DAO governance. We develop and evaluate a novel AI-augmented decision-making framework that combines large language models, multi-agent reinforcement learning, and human input. Our experiments involve three distinct DAO structures: a DeFi protocol DAO, a social impact DAO, and a research-focused DAO. We implement our AI-augmented framework alongside traditional voting mechanisms and compare their performance across multiple metrics, including decision quality, time-to-consensus, and participant satisfaction.Results indicate that the AI-augmented approach significantly enhances decision-making processes. In particular, we observe a 28% improvement in decision quality (as measured by subsequent DAO performance indicators), a 35% reduction in time-to-consensus, and a 22% increase in member participation rates. Notably, the AI system demonstrated an ability to synthesize diverse viewpoints, propose novel solutions, and provide context-aware information to DAO members, effectively augmenting collective intelligence. However, our study also reveals potential challenges, including the risk of AI systems amplifying existing biases, the need for careful design of AI-human interfaces, and concerns about long-term effects on member engagement and skill development. We discuss these ethical considerations and propose a framework for responsible implementation of AI in DAO governance.This research contributes to the growing body of work on AI governance and decentralized systems, offering insights into how artificial intelligence can enhance collective decision-making in DAOs. Our findings have implications for the future design of DAO structures and suggest new directions for research in AI-human collaboration in decentralized governance contexts.