Automation of Legal Precedents Retrieval: Findings from a Literature Review

Judges frequently rely their reasoning on precedents. Courts must preserve uniformity in decisions while, 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. This literature review offers a comprehensive analysis of the advancements in automating the identification of legal precedents, primarily focusing on the paradigm shift from manual knowledge engineering to the incorporation of Artificial Intelligence (AI) technologies such as natural language processing (NLP) and machine learning (ML). While multiple approaches harnessing NLP and ML show promise, none has emerged as definitively superior, and further validation through statistically significant samples and expert-provided ground truth is imperative. Additionally, this review employs text-mining techniques to streamline the survey process, providing an accurate and holistic view of the current research landscape. By delineating extant research gaps and suggesting avenues for future exploration, this review serves as both a summation and a call for more targeted, empirical investigations.

Automated summary

This literature review provides a comprehensive analysis of the advancements in automating the identification of legal precedents, with a focus on the shift from manual knowledge engineering to incorporating AI technologies such as natural language processing and machine learning. While various approaches using NLP and ML show promise, no definitive superior method has emerged yet, and further validation is necessary. Additionally, text-mining techniques are employed to streamline the survey process and offer an accurate and holistic view of the current research landscape.