Making Sense of Legal Jargon with AI

AI is shaking up the legal world, making it easier to understand complex laws and predict legal outcomes. Traditional AI models struggle with the intricate language and reasoning needed for legal tasks. This research introduces a new AI model called LexFaith-HierBERT. It aims to identify specific legal violations and the articles or rights that have been breached. The model combines a hierarchical BERT-based encoder with a relational rationale head and a faithfulness-aware attention mechanism. This setup helps the AI understand both the relationships between words and the context within sentences, making its predictions more transparent.The LexFaith-HierBERT model outperforms other AI models, including machine learning and deep learning methods. It achieves an accuracy of 88% for binary classification and a micro-F1 score of 71% for multi-label classification. Statistical tests confirm the model's reliability in real-world legal applications. To make the model's decisions more understandable, it uses LIME, SHAP values, and attention heatmaps. These tools help explain how the model arrives at its conclusions, making it more transparent and trustworthy. The legal field is full of complex language and reasoning. AI models like LexFaith-HierBERT can help make sense of this jargon. By improving the accuracy and transparency of legal predictions, AI can assist legal professionals in their work. This research shows that AI has the potential to revolutionize the legal field, making it more efficient and accessible.