A Law School Course in Applied Legal Analytics and AI

Abstract

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, the ways text analytics have been applied to support the work of legal professionals, researchers, and administrators, and the techniques for evaluating how well they work.
 The 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. Then it describes the 2020 version of the course, including the students, instructors, and course sessions in overview. We explain how we taught law students skills of programming and experimental design and engaged them in assignments that involve using Python programming environments to analyze legal data.
 The course culminated in joint projects engaging small teams of law and technical students in applying machine learning and data analytics to legal problems. The article explains how the instructors prepare the students for the final course projects, beginning early in the term with project ideas and databases of text, forming teams, working on the projects as a team and obtaining interim feedback, and finally completing the projects and reporting results. We draw some salient comparisons between the 2019 and 2020 versions of the course and report what worked well and what did not, the students’ reactions, and lessons learned for future offerings of the course.