Abstract: The integration of artificial intelligence (AI) into legal systems is redefining the landscape of governance, compliance, and policy enforcement. This research proposes a novel framework for AI-driven legal decision models designed to interpret, automate, and enforce regulatory policies in real time. Leveraging natural language processing (NLP), symbolic reasoning, and predictive analytics, the proposed architecture codifies legal norms into machine-executable logic to facilitate dynamic and transparent decision-making across diverse administrative contexts.The methodology involves the development of rule-based ontologies and legal knowledge graphs aligned with statutory databases, complemented by machine learning algorithms trained on judicial outcomes and compliance datasets. Performance was evaluated using accuracy, precision-recall metrics, and explainability benchmarks across simulated regulatory scenarios. The proposed system demonstrated a 93.6% accuracy in automated legal reasoning tasks and reduced average compliance processing time by 41% compared to conventional manual review systems.Key implications include enhanced operational efficiency, reduced human bias, and improved legal transparency in government and enterprise environments. Furthermore, the model supports continuous legal updates and real-time policy adjustments via automated regulatory intelligence. By embedding AI within the core of legal infrastructure, this study sets a foundational precedent for scalable, accountable, and responsive legal automation systems that align with democratic principles and rule-of-law standards. Keywords AI in Law, Legal Decision Models, Policy Automation, Regulatory Intelligence, Legal Ontologies, Governance Technology, Rule-Based Systems, Real-Time Compliance, Explainable AI, Judicial Informatics
Murali Krishna Pasupuleti (Thu,) studied this question.