An Intelligent Predictive Fairness Model for Analyzing Law Cases with Feature Engineering

Artificial intelligence (AI) is transforming numerous sectors, and its integration into the legal domain holds significant potential for automating labor-intensive tasks, enhancing judicial decision-making, and improving overall system efficiency. This study introduces an AI-powered model, named the Legal Judgment Prediction Ensemble (LJPE), which is designed to predict legal case outcomes by leveraging historical judicial data. By using natural language processing (NLP) techniques, feature engineering, and a complex two-level stacking ensemble, the LJPE model has better predictive accuracy at 94.68% compared to modern legal language and conventional machine learning models. Moreover, the findings underline the predictive strength of textual features obtained from case facts, vote margins, and legal-specific features. This study offers a solid technical solution for predicting legal judgments for the responsible use of the model, helping to create a more efficient, transparent, and fair legal system.