QOC DAO -- Stepwise Development Towards an AI Driven Decentralized Autonomous Organization

TL;DR

This paper proposes a structured, stepwise governance framework for DAOs that integrates the QOC model with AI agents, enhancing transparency, fairness, and scalability in decentralized decision-making.

Contribution

It introduces a novel combination of the QOC decision model with AI agents to improve DAO governance, moving from human-led to fully autonomous processes.

Findings

QOC model improves transparency and fairness in DAO voting

AI agents support or automate decision evaluations effectively

Statistical safeguards help detect manipulation

Abstract

This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO) through the integration of the Question-Option-Criteria (QOC) model and AI agents. We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes. By decomposing decisions into weighted, criterion based evaluations, the QOC model enhances transparency, fairness, and explainability in DAO voting. We demonstrate how large language models (LLMs) and stakeholder aligned AI agents can support or automate evaluations, while statistical safeguards help detect manipulation. The proposed framework lays the foundation for scalable and trustworthy governance in the Web3 ecosystem.