AI-Driven Prediction of Indian Criminal Case Outcomes

Indian courts have too many cases and not enough resources or judges, causing delays and undermining trust in the legal system. In addition, Indian law is quite complex, requiring law students and legal professionals to study and understand it thoroughly for better practice. To address these challenges, we have developed an AI-powered legal assistant designed to enhance the efficiency and effectiveness of legal processes. The three main features of our proposed approach are judgment prediction, case summarization, and IPC section and punishment prediction. The judgment prediction uses AI & ML to analyze case facts and give the probability of each party winning. The case summarization compacts the long legal documents into concise texts, highlighting key legal terms and facts. The IPC section and punishment prediction identifies applicable sections of the Indian Penal Code and predicts. These features enhance legal workflows, reduce manual document analysis and improve legal decision-making. Our proposed legal AI system is tested by deploying on Streamlite, which provides interactive interface that can be used on any device with internet access. The performance of the proposed AI model is analysed and the general accuracy of the classification model is obtained as 97%. The performance can be further improved using a more comprehensive and diverse dataset. The consistency and reliability of the model are validated by computing macro- and weighted averages, affirming its robust performance across all IPC categories. In addition, the precision, recall, and F1 score values indicates the accurate identification of winning parties.