Human Rights Case Analysis Using Ai and Transformer Models

Abstract

The increasing volume of human rights cases calls for intelligent systems capable of analyzing legal documents efficiently and accurately. Manual review is time-consuming, labor-intensive, and susceptible to human error, making it challenging to detect patterns or similarities across extensive datasets. This project leverages AI techniques—specifically Transformer-based Legal-BERT and Doc2Vec combined with Support Vector Machine (SVM)—to automate the analysis of human rights case documents. These methods enable semantic understanding, feature extraction, and classification of legal texts, assisting stakeholders in identifying violations and relevant precedents more effectively. Legal-BERT, a BERT variant pretrained on legal corpora, captures domain-specific language, legal terminology, and contextual nuances, generating high-quality embeddings for case documents. This model supports tasks such as case classification, violation detection, and semantic similarity analysis, allowing the system to identify related cases and recurring patterns in human rights violations. In parallel, Doc2Vec transforms entire documents into vector representations, which are subsequently classified using SVM to predict categories or potential case outcomes. By combining deep contextual embeddings with robust traditional machine learning, this hybrid approach enhances overall performance. The proposed system not only accelerates human rights case analysis but also ensures scalability and continuous learning as new cases are incorporated. By integrating Legal-BERT with Doc2Vec + SVM, the project establishes a comprehensive AI-powered framework for legal document processing, pattern recognition, and decision support, thereby improving the efficiency of legal professionals, NGOs, and policymakers in protecting human rights.