Human Right

Human rights research increasingly leverages natural language processing (NLP) to analyze large legal datasets, such as those from the European Court of Human Rights, aiming to improve the efficiency and accuracy of identifying and classifying human rights violations and related legal precedents. Current research focuses on developing robust and explainable models, often employing techniques like topic modeling, citation network analysis, and various deep learning architectures (e.g., BERT variants), to enhance the reliability of case outcome prediction and vulnerability detection. This work has significant implications for legal scholarship, human rights advocacy, and the development of AI systems that are ethically aligned with human rights principles, facilitating more effective monitoring, analysis, and ultimately, protection of human rights globally.