A Law School Course in Applied Legal Analytics and AI

Technological advances in artificial intelligence (AI) are affecting the legal profession. Machine learning (ML) and natural language processing (NLP) enable new legal apps that, to some extent, can analyze contracts, answer legal questions, or predict the outcome of a case or issue. While it is hard to predict the extent to which these techniques will change law practice, two things are certain: legal professionals will need to understand the new text analysis techniques and how to use and evaluate them, and law faculties face the question of how to teach law students the required skills and knowledge to do so. At the University of Pittsburgh School of Law, the authors have co-designed a semester-long course entitled ‘Applied Legal Data Analytics and AI’, and twice taught it to combined groups of law students and students from technical departments. The course provides a hands-on practical introduction to applying ML and NLP to extract information from legal text data. It demonstrates applications of text analytics that support the work of legal professionals, researchers, and administrators and techniques for evaluating how well the new tools work. The course culminated in joint projects engaging small teams of law and technical students in applying machine learning and data analytics to legal problems. This article introduces the new text analytic techniques and briefly surveys law schools’ current efforts to incorporate instruction on computer programming and machine learning in legal education. It then provides an overview of the course and explains how we taught law students skills of programming and experimental design that prepared them for the final course projects.

Recommended citation: Savelka, Jaromir, Matthias Grabmair, and Kevin D. Ashley. "A law school course in applied legal analytics and AI." Law in Context: A Socio-Legal J. 37 (2020): 134.
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