Automation of legal precedents retrieval: findings from a rapid literature review

Abstract

Abstract Judges frequently rely their reasoning on precedents. In every circumstance, courts must preserve uniformity in case law and, depending on the legal system, previous cases compel rulings. The search for methods to accurately identify similar previous cases is not new and has been a vital input, for example, to case-based reasoning (CBR) methodologies. Innovations in language processing and machine learning (ML) brought momentum to identifying precedents while providing tools for automating this task. This rapid literature review investigated how research on the identification of legal precedents has evolved. It also examined the most promising automation strategies for this task and confirmed the growing interest in using artificial intelligence for legal precedents retrieval. The findings demonstrate that no artificial intelligence solution currently stands out as the most effective at finding past similar cases. Also, existing results require validation with statistically significant samples and ground truth provided by specialists. In addition, this work employed text mining (TM) to automate part of the literature review while still delivering an accurate picture of research in the field. Ultimately, this review suggests directions for future work, as more experimentation is required.