Spring 2019 Course Website

CMU: Course number 11-646 (graduate), 11-546 (undergraduate), 12 units
Pitt: Law 5719, Class # 30523, 3cr
Times: Mondays 10:30-11:45pm, CMU Porter Hall 125C; Wednesdays 10:30-11:45pm, Pitt Barco Law Building G20

Instructors

Matthias Grabmair, Systems Scientist, CMU LTI
Kevin D. Ashley, Professor of Law and Intelligence Systems, University of Pittsburgh

Course Abstract

Technological advances are affecting the legal profession and enable innovation by experts proficient in both law and AI technology. This joint course, co-taught by instructors from the University of Pittsburgh School of Law and Carnegie Mellon University’s Language Technologies Institute, provides a hands-on practical introduction to the fields of artificial intelligence and law, machine learning, and natural language processing as they are being applied to support the work of legal professionals, researchers, and administrators, such as extracting semantic information from legal documents and using it to solve legal problems. Meanwhile, LegalTech companies and startups have been tapping into the industry’s need to make large-scale document analysis tasks more efficient, and to use predictive analytics for better decision making. This course is intended to bring students of law and technical disciplines together into a collaborative classroom setting to learn about the technologies at the intersection of law and AI through lectures, class discussion of relevant material, data analysis assignments, as well as to gain practical experience through collaborative project work. Topics in focus include machine learning and natural language processing applied to legal data, fair machine learning, and selected legal issues that relate to AI technologies. Students should come from either a (pre-) law background with a strong interest in gaining practical experience with legal analytics, or from a technical discipline with an equally strong interest in tackling the challenges posed by legal analytics tasks and data.

Course Topics

The course will cover the following topical progression:

  • Python Programming Tutorial
  • Introduction to Artificial Intelligence & Law, including formal rule- & case-based reasoning, and computational models of argumentation
  • Basics of Machine Learning
  • Analysis and Prediction using the Supreme Court Database
  • Fair Machine Learning under Nondiscrimination Imperatives
  • Basics of Natural Language Processing
  • Legal Text Analytics including annotation, rule-based & ML-based text processing, and information retrieval
  • Legal Topics Related to AI including Legal Liability of Autonomous Vehicles and AI & Data Privacy

Learning Outcomes

After completing this course, and depending on students’ focus in the course project, they will have gained:

  • an understanding of knowledge representation and argumentation formalisms used in AI&Law
  • an understanding of and practice with basic techniques in applied machine learning
  • practical experience in the development and assessment of research hypotheses in legal data analytics
  • practical experience in design, planning and critical evaluation of legal data analytics project work
  • basic knowledge in legal issues arising in the context of AI technology

Course Format & Project Requirement

The course will be a mixture of lectures and group discussions of assigned reading, for which students are required to submit short abstract hand-ins. There will also be one legal research and writing assignment, as well as three programming assigments which require both individual and pairwise work. In the second half of the semester students will form mixed teams of “lawyers” and “engineers” and propose a final project on legal data analysis which they will work on collaboratively. The course ends with project presentations and a final report. There will be no midterm or final exam.

Student Audience and Prerequisites

  • University of Pittsburgh School of Law second and third-year students with either prior experience in programming with the Python language or a willingness to learn it in the first weeks of the semester. Some experience in basic probability and statistics will also be beneficial.
  • Carnegie Mellon University undergraduate students (juniors and seniors) with a pre-law major or course record with programming proficiency or willingness to learn it, and graduate students in technical disciplines from CMU or Pitt’s School of Computers and Information with a strong interest in learning about the law, legal reasoning, and challenges in applying data science and AI methods to legal data.
  • If you are interested in taking the course and are unsure whether you are eligible or are sufficiently prepared, please contact the instructors.