Towards Fair and Scalable DAO Governance: An NLP-Driven Scoring System for Proposal Evaluation

Abstract

The Decentralized Autonomous Organizations (DAOs) are shaping the future of the governance by moving away toward the power of the communities making calls without a central body. Nevertheless, it is becoming harder to assess the quality of the proposals as those are increasing and the number of demands is increasing as well. Manual reviews need more man power, lack consistency and are prone to bias because each one can produce varying levels of clarity, possibility, and fit within organizational objectives. In this paper, we introduce the proposal evaluation system based on AI, which uses transformer-based Natural Language Processing (NLP) models and Explainable AI (XAI) to automate and interpret the assessments of DAO proposals. The system scores in three dimensions, including impact, feasibility, and goal alignment in a clear and continuous way, with the justifications in human-readable formats. Our solution promotes both the fairness and scalability of decision-making in DAOs by decreasing voter fatigue and achieving a more straightforward workflow in governing the activities. Trained and validated on real-world DAO proposal datasets, the model delivers high performance regarding accuracy, explainability, and user trust. The contribution of this project is to guide the community to the intelligent systems of governance in Web3 through how to increase the transparency and trust in them using automated decision-support tools leaving the decentralized nature of DAOs intact.