Explainable judgment prediction and article-violation analysis using deep LexFaith hierarchical BERT model

The use of Artificial Intelligence has already changed every sphere of our lives and legal field is not an exception as cases are being prepared by analyzing content of legal documents. Traditional machine learning and deep learning models lack to comprehend the complex language and infer legal reasoning required in such tasks. In this research work, our aims to predict which legal violation has occurred and also to specific articles or legal rights have been violated. To achieve the objectives, we propose architecture named Legal Faithfulness-Aware Hierarchical BERT (LexFaith-HierBERT), which integrates a hierarchical BERT-based encoder with a relational rationale head and a faithfulness-aware attention mechanism. The proposed model captures both inter- and intra-token dependencies, offering deeper contextual understanding and thus improved transparency in predictions. The proposed approach significantly outperforms several baseline methods from the existing studies including machine learning and deep learning including transformers. Empirical results demonstrate that proposed model achieves the highest accuracy of 88% for binary classification and a leading micro-F1 score of 71% for multi-label classification. Statistical significance tests prove legal reliability of the proposed system in real-world applications and model interpretation is carried out using LIME, SHAP values and attention heatmaps, to enhance the transparency and explainability of proposed model for decision making.