AI-Augmented Local Governance: A Computational Framework for Enhancing Administrative Efficiency and Participatory Democracy

Abstract

Background: Local self-governments worldwide face increasing pressure to deliver efficient, transparent, and participatory services while managing limited resources. Administrative bottlenecks, inconsistent decision-making, and low citizen engagement remain persistent challenges. Objective: This paper explores how artificial intelligence (AI) and computational methods can be systematically integrated into local governance structures to improve decision-making, automate routine administrative tasks, and enhance citizen engagement. Methods: We propose a three-layer computational framework called Local Brain comprising (1) a data aggregation and preprocessing layer (using NLP, OCR, and entity recognition), (2) a decision support layer (using supervised learning, rule-based reasoning, and explainable AI techniques), and (3) a citizen interaction layer (using conversational AI, sentiment analysis, and recommendation systems). The framework is validated through a discrete-event simulation based on public data from three municipalities of different sizes over a 12-month period. Results: The framework reduces administrative processing time by 43.2% (p < 0.001, Cohen’s d = 1.87), increases decision consistency by reducing variance in approval rates by 60%, and raises citizen participation rates by 28.4% in digital consultations. The chatbot alone resolves 72% of routine inquiries without human intervention. Conclusion: AI offers measurable, statistically significant improvements in local governance efficiency and democratic participation. However, success depends critically on transparent algorithm design, data privacy safeguards, human-in-the-loop oversight, and regulatory alignment. The paper concludes with a detailed policy roadmap and technical recommendations for municipalities considering AI adoption.