AI-Based Assistant for Generating and Analyzing Legal Documents

Abstract

The legal industry has been heavily impacted by fast-paced digitization, but the complete volume of complex legal documents, technical jargon, and arcane procedures continues to limit access to legal knowledge for both professionals and the general public. This highlights the need for more powerful, AI-based solutions for the retrieval and comprehension of legal information. The state-of-the-art AI and natural language processing (NLP)-based legal assistance systems are designed to increase accessibility, but are limited in their practical impact, scalability, and explainability. In this paper, we present an AI-assisted tool for the generation and analysis of legal documents. The system is based on a fine-tuned Distil-BERT model for effective classification of legal documents, semantic comprehension, and user-centric legal information support. The framework fulfils data preprocessing, transformer-based feature extraction, and supervised classification in an end-to-end manner, and can be embedded in legal service applications. The experimental results on a legal document dataset show that the proposed Distil-BERT model achieved an accuracy of 82.01% and a mean F1 of 84.32%. These results demonstrate that distillation-based knowledge transformers can achieve better generalization with less complexity, which is a desirable property for scalable solutions in the legal AI context. Further, we present important ethical and deployment factors to consider (e.g., data privacy, legal liability of AI outputs, and algorithmic bias mitigation). In sum, the proposed system takes a step toward intuition-friendly, efficient, and ethically conscious AI-supported legal aid that furthers the digital transformation of legal services.