Improving the trial efficiency of criminal cases with the assistance of artificial intelligence

Abstract This study explores the application of artificial intelligence (AI) in improving the trial efficiency of criminal cases. Using a dataset consisting of 500 criminal case records, including minor, ordinary, and complex cases, we applied machine learning (ML) and natural language processing (NLP) techniques to predict trial outcomes, reduce processing time, and improve judgment accuracy. The ML models, such as decision tree regression and support vector machines (SVM), were trained on historical case data to predict trial time and verdict accuracy. NLP was used to automate document generation and extract key legal information from trial records. Results showed that AI-assisted trials reduced average trial time by 40% and reduced error rates by 55% compared to traditional methods. The findings indicate that AI can significantly enhance judicial efficiency, but challenges related to AI implementation, scalability, and bias mitigation remain. Future research should focus on testing AI systems in diverse judicial contexts to address these issues.