Applied Legal Data Analytics & AIClass Term:
Spring Term 2021-2022Catalog Number:
3 (3 Contact, 0 Field)Priority:
General Enrollment CourseFull Year Course:
Final grades will consist of the course project (40%), four assignments (4x10%=40%), and the paper abstract submissions (20%).
Technological advances are affecting the legal profession. While it is hard to predict the changes that machine learning and natural language processing will bring, legal professionals certainly will need to understand the new techniques and how to use and evaluate them. This course, co-taught by instructors from the University of Pittsburgh School of Law and Intelligent Systems Program, provides a hands-on practical introduction to the fields of artificial intelligence, machine learning and natural language processing as they are being applied to support the work of legal professionals, researchers, and administrators. Researchers in the field of Artificial Intelligence and Law (AI&Law) have been applying recent advances in natural language processing and machine learning to extract semantic information from legal documents and to use it to solve legal problems. Meanwhile, the commercial LegalTech sector is thriving. Companies and startups have been tapping into the legal industry’s need to make large-scale document analysis tasks more efficient, and to use predictive analytics for better decision making. This course will help law students gain literacy with these technologies and learn how to apply them to the kinds of legal problems they have studied or will encounter in practice. This course not only teaches law students about the new tools, but enables students to gain practical experience using them under close mentorship and in project-based collaboration with students from computer science backgrounds who want to learn about the law. Lecture sessions will alternate with working sessions where instructors assist groups with projects.
This course has been flagged as a distance education course. This means this class is one in which students are separated from the faculty member or each other (other than specially accommodated students) for more than one-third of the instruction and the instruction involves the use of technology to support regular and substantive interaction among students and between the students and the faculty member, either synchronously or asynchronously.