DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR LEGAL CASE ANALYSIS AND DECISION MAKING USING RECURRENT NEURAL NETWORK

Abstract

Over the last few decades, there have been substantial developments in a variety of domains, including computer science, artificial intelligence, and machine learning, which has accelerated the evolution of intelligent systems such as decision support systems with applications in fields such as healthcare, finance, manufacturing, transportation and legal systems. Decision Support Systems (DSS) in legal systems also known as Judicial Decision Support Systems (JDSS) are designed to assist judges and other legal professionals in making legal decisions. JDSS can be used in various areas of law, such as criminal law, civil law, and family law. JDSS can be helpful in various ways, such as reducing the time and effort required to make legal decisions, increasing consistency and fairness in legal decisions, and providing judges and legal professionals with access to relevant legal information and expertise. However, JDSS also have limitations and challenges, such as the need for accurate and up-to-date legal data and the potential for bias in the data or algorithms used in the system. This research work developed a system to assist legal practitioners and it is also designed to enhance the efficiency, transparency, and accessibility of the judicial process. The performance of the developed system was evaluated using some metrics and the following results were obtained: accuracy of 80%, 87% precision, 87% sensitivity/recall and an F1-score of 87%.