JusticeAI: A Large Language Models Inspired Collaborative and Cross-Domain Multimodal System for Automatic Judicial Rulings in Smart Courts

• Published in: IEEE access 2024, Vol.12, p.173091-173107
• Main Authors: Abdel Samee, Nagwan, Alabdulhafith, Maali, Muhammad Ahmed Hassan Shah, Syed, Rizwan, Atif
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Data models; Decision making; Deep learning; Europe; Law; multimodal networks; Natural language processing; Predictive models; smart courts; Support vector machines; transformers
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3491775

There has been a significant amount of attention in recent years toward the utilization of artificial intelligence (AI) in the realm of legal decision-making. This growing pattern reveals a higher interest among academics and legal professionals in utilizing AI technologies to enhance a number of legal system components. Artificial intelligence (AI) tools, such as machine learning and natural language processing, possess the capacity to analyze vast quantities of legal data, extract valuable insights, and facilitate decision-making processes. The primary aim of this study is to develop a sophisticated framework for judicial decision-making that incorporates methodologies from artificial intelligence and utilizes the dataset from the European Court of Human Rights (ECHR). The utilization of this methodology holds promise in improving the decision-making procedures of legal professionals and reducing the laborious task of manually analyzing legal documents. As a result, this can lead to the facilitation of more accurate predictions of court rulings. Our research introduces a hybrid ensemble model designed specifically for smart court rulings. This innovative approach harnesses the benefits of pre-trained embeddings and large language models to accurately predict court decisions. By utilizing the power of pre-existing embeddings and incorporating the capabilities of advanced language models, our proposed model demonstrates enhanced predictive accuracy and efficiency in the context of court rulings. We also focus on the models' feasible interpretability and highlight their ability to determine key factors in legal decision-making. We attain a notably high accuracy score of around 83%. Our research illuminates how large language models (LLMs) and advanced deep learning techniques can be utilized to predict legal outcomes.