TECHNOLOGIES, THEORIES AND DEVELOPMENTS: MODERN SCIENTIFIC TEACHING

Governments and policymakers increasingly rely on data-driven tools to evaluate regulations and forecast policy impacts.Large language models (LLMs) show potential for interpreting legislative texts but face issues of hallucination and lack of transparency.We propose GovAI, a framework integrating chain-of-thought prompting, a Policy Reasoning Map (PRM), and abstention mechanisms tuned with expert feedback.Experiments on synthetic policy datasets show GovAI improves accuracy of legal-policy alignment, interpretability, and reduces unsafe recommendations.1 Introduction Policy analysis involves synthesizing legislative documents, stakeholder reports, and economic data.Errors in reasoning can have far-reaching consequences, including flawed regulations or misallocation of public resources.LLMs are increasingly applied in summarizing bills, analyzing legal texts, and simulating policy outcomes.However, their lack of transparent reasoning and risk of hallucination hinder adoption in governance contexts.GovAI provides a framework for transparent and regulation-aware reasoning in policy analysis, combining structured reasoning with abstention mechanisms.This builds upon similar directions in finance and auditing research.Hu and Hong investigated temporal reasoning [1], Hong and Hu designed hierarchical graphtemporal models [2], Hu and Mehra developed AuditGuard [3], and Hu and Lin created explainable fraud analysis [4].These works emphasize interpretability in high-stakes contexts, inspiring GovAI.2 Related Work Traditional policy evaluation relied on econometric models and rule-based simulations, which lacked scalability and explainability.LLMs have been applied to legislative summarization and legal clause retrieval but without structured reasoning alignment to standards.Explainable AI methods show promise but rarely ensure compliance with legal frameworks.GovAI adapts trustworthy AI techniques to governance.3 Methodology GovAI integrates three parts: (1) chain-of-thought reasoning, (2) Policy Reasoning Map (PRM) linking steps to regulatory codes, (3) rejection mechanism trained with expert policymaker feedback.PRM ensures each inference is tied to authoritative legislative references.RLHF tunes abstention thresholds.