Return of the AI: An analysis of legal research on Artificial Intelligence using topic modeling

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AI research finds itself in the third boom of its history, and in recent years, AI-related themes have gained considerable popularity in new disciplines, such as law. This paper explores what legal research on AI constitutes of and how it has evolved, while addressing the issues of information retrieval and research duplication. Using Latent Dirichlet Allocation (LDA) topic modeling on a dataset of 3931 journal articles, we explore three questions: (a) Which topics within legal research on AI can be distinguished? (b) When were these topics addressed? and (c) Can similar papers be detected? The topic modeling results in a total of 32 meaningful topics. Additionally, it is found that legal research on AI drastically increased as of 2016, with topics becoming more granular and diverse over time. Finally, a comparison of the similarity assessments produced by the algorithm and a human expert suggest that the assessments often coincide. The results provide insights into how a legal research on AI has evolved over time, and support for the development of machine learning and information retrieval tools like LDA that assist in structuring large document collections and identifying relevant articles.

Original languageEnglish
Title of host publicationProceedings of the Natural Legal Language Processing Workshop 2020
EditorsNikolaos Aletras, Ion Androutsopoulos, Leslie Barrett, Adam Meyers, Daniel Preoţiuc-Pietro
Number of pages8
Publication statusPublished - 24 Aug 2020

Publication series

SeriesCEUR Workshop Proceedings

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