Utilizing AI and Natural Language Processing to Predict Supreme Court Decisions in Thailand

Abstract

Classical approaches frequently prove inadequate in accurately forecasting court outcomes within the intricate realm of legal decision-making. The Thai legal system presents an especially formidable obstacle, given the cultural nuances and legal jargon that contribute to its overall intricacy. This study attempts to fill this gap by investigating the utilization of artificial intelligence (AI) and natural language processing (NLP) in forecasting Thai Supreme Court decisions. In this work, we introduce an innovative methodology that integrates sophisticated text analytics, machine learning algorithms, and an extensive collection of Thai Supreme Court decisions. Our methodology consists of preprocessing legal texts, employing machine learning models to forecast court outcomes, and extracting pertinent features utilizing NLP techniques. The results of the research indicate that the state-of-the-art deep learning model called WangchanBert has considerable potential to improve the forecasting of judicial rulings in Thailand in various metrics. This research not only enhances the expanding domain of artificial intelligence in the legal sector but also offers pragmatic perspectives for legal professionals and scholars with an interest in the Thai legal system. The study's findings have broader implications that transcend the Thai context, providing a framework for analogous implementations in other legal jurisdictions.