Designing an AI-Powered Framework for Detecting Systemic Bias in Judicial Decision-Making Processes Using Legal Text Analytics

This research proposes a high-accuracy bias detection framework for judicial decision-making by combining transformer-based legal NLP models with fairness-aware machine learning techniques. Using LegalBERT and structured demographic annotations, the model was evaluated on diverse judicial corpora across sentencing, bail, appeals, and civil litigation cases. It achieved consistent classification accuracies exceeding 95.4%, with F1-scores reaching 0.95 and fairness scores up to 0.94. Compared to baseline models, the enhanced framework reduced classification disparity by over 23% and improved interpretability through SHAP and attention-based visualizations. Integration of contextual embeddings, demographic features, and bias-sensitive scoring mechanisms enabled sentence-level analysis of judicial reasoning, surpassing rule-based and statistical benchmarks. Unlike computationally expensive deep learning pipelines, the proposed model maintained training runtimes under 1.5 seconds, ensuring operational feasibility for legal tech platforms. The framework supports cross-jurisdictional scalability and is adaptable to multiple case types without architectural modifications.