Artificial Intelligence using ANN on Legal Regulations in India: A Comprehensive Overview

Background: More and more artificial intelligence (AI) has lately been applied in many various disciplines, including the legal one, to increase efficiency and decision-making. Objective: This paper looks at how Indian legal laws could be analysed and understood using artificial neural networks (ANN). Great numbers of statutes, precedents, and interpretations define India’s complex legal environment. Conventional manual legal document analysis is sometimes timeconsuming and prone to human error; so, automated methods that can provide more accurate and efficient analysis are much needed. Methodology: We developed a model utilising ANN to manage this challenge by analysing and classifying legal documents depending on their content, context, and application to particular regulatory systems. The method consists in tagged with domain-specific tags training a neural network on a large collection of legal documents comprising statutes, case laws, and legal commentary. Using modern optimisation techniques including dropout regularisation and backpropagation, the ANN model was modified to increase its generalising capacity. According to the results, accuracy and efficiency have greatly raised themselves as compared to more traditional methods. Results and Findings: In determining legal language, the ANN model achieved an accuracy of $\mathbf{9 2. 3 \%}$, a precision of $\mathbf{9 0. 1 \%}$, a recall of 91.7%, and an $F 1$ score of 90.9%. The model also cut the time required for legal document interpretation, by roughly 45% as compared to traditional methods. Conclusion: These findings demonstrate how fast and consistently artificial intelligence can grasp legal texts, hence transforming legal analysis and regulatory compliance in India.