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Teaching Legal Analytics and AI to Law Students

of the Talk Around the globe, law school faculties are considering whether and how to prepare students for the effects of data analytics on legal practice. Data analytics apply natural language processing and machine learning to extract semantic information from legal documents and use it to solve legal problems. Legal professionals need to understand how to use and evaluate the new techniques; the question is how and how much to teach law students about computer programming, machine learning, and data analytics. Professor Ashley provides an overview of current efforts in U.S. legal education to incorporate instruction in these topics and skills. His new course at Pitt, Applied Legal Analytics and AI, developed with his co-instructors, Matthias Grabmair and Jaromir Savelka, is designed to help law students to gain literacy with the new technologies and provide hands-on practical experience in applying them to legal data. The course includes law students and graduate students with computer science and engineering backgrounds. It introduces using machine learning and natural language processing techniques to analyze legal data. Joint projects engage small teams of students in applying the learned techniques to legal corpora and evaluating the results. Ashley summarizes the course curriculum and instructional goals, reports on what worked and what did not, the students’ reactions, lessons learned, and future plans for the course. Copyright c © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).