This course explores connections between law and computing, with a focus on artificial intelligence and machine learning.
This is a jointly taught course, and the LAW 7127 and CS 4501 courses will meet together and mostly have the same assignments. For many of the assignments, including the final project, Law and CS students will be working closely together in a team. This syllabus applies only for the CS section, though; the LAW class has a different syllabus.
This class will be different from typical CS courses, and students in the class are expected to be open to learning new things in different ways in a different academic culture, as well as to sharing and explaining your computer science expertise with Law students. We also expect CS students to learn some new computing concepts, techniques, and skills in the course.
Expected Background. To enroll in cs4501 the expected background is:
Formal Prerequisite: completed CS 2100 (DSA1) or CS 2130 (CSO1).
Informal Prerequisite: Students are not required to have machine learning background, but should be comfortable enough with programming and math concepts to be able to learn new APIs on their own and to explain computing concepts to Law students.
No previous background in law is expected, but students should be willing to read legal writings and open to developing legal ways of thinking.
This course meets jointly with a Law School class, and CS students will work closely with Law students throughout the semester. The CS and Law sections of the course are graded separately, following different grading policies and conventions.
Meetings. Friday mornings, 9:00-11:30am in WB 152 (at the Law School).
Teachers. The course is jointly taught by Thomas Nachbar (Professor of Law) and David Evans (Professor of Computer Science).
Materials. Materials for the class will be posted on the course website or in canvas and will include both legal and computer science readings.
Students are expected to bring a laptop to each class session (if you have a laptop problem, the CS department has a limited number of loaner machines available for students who need them).
Readings. There will be readings assigned for each class meeting that should be read before the class. Most readings will be required for both the CS and Law students, but in some cases readings that are required for the Law students that are optional for the CS students, as well as readings that are required for the CS students that are not required for the Law students. Students are expected to read and think deeply about the readings before class, and be prepared to discuss the readings during class.
Pre-Class Assignments. Most weeks, there will be a short assignment due about the readings. These will be due 5:00pm on Thursdays (the day before class). The pre-class assignments will typically just be answering a few short questions or stating your own questions about the readings. Your responses to the pre-class assignments will help set-up the in-class discussion and ensure you are well prepared to contribute to it.
Post-Class Assignments. There will often be post-class assignments where students are expected either to complete or extend an exercise started during class, or to provide short answers to questions raised in the class.
Class Contribution. Students are expected to contribute actively and constructively during class sessions through discussion, dialog, and participation in in-class exercises. If you cannot attend a specific class, you should contact me (evans@virginia.edu) as early as possible. Because we will be assigning students to work in groups, it is essential that you let us know when you are unable to attend class so that we can adjust group assignments accordingly.
Mid-Term Exam. There will be an in-class exam on October 2 (about an hour long) covering topics from the readings and classes for the first 5 weeks (through September 25).
Final Project. Students will work in a small team on an open-ended project related to the goals of the course. There will be several intermediate deliverables, draft presentations scheduled during classes toward the end of the semester, and a final presentation scheduled with the course instructors during the final exam period.
Grading. Grading is done independently for the CS4501 and LAW7127 courses. Final grades will be this default distribution:
| Item | Default |
|---|---|
| Class Contribution | 20% |
| Individual Assignments | 30% |
| Midterm Exam | 20% |
| Final Project | 30% |
This weighting will be used to compute a minimum grade, but there is some flexibility in how different aspects of the course will be weighted to reflect exceptional performance on particular items. For example, Class Contribution can count for more than 20% for a student who makes consistently valuable contributions to the class, and an outstanding final project may overcome weaknesses in earlier assignments.
This course explicitly engages the use of AI tools as a part of its course of study and we believe that it is important for everyone take appropriate advantage of the remarkable capabilities of these tools while understanding enough about how they work to appreciate potential pitfalls and the risks of inappropriate AI tool use. Consequently, some portions of assignments will explicitly require the use of those tools while others may place constraints on their use and require you to document how you used then, and others will explicitly prohibit their use.
Each assignment will include a description of the acceptable uses of AI tools which we will aim to make clear and understandable. However, potential uses often fall into grey areas (some of which we will explore in this class), so it may not always be clear what is and is not appropriate. If at any time you have questions about whether you may use an AI tool, you should contact one of the instructors for clarification. Because we will be actively using these tools, it is not possible to provide a blanket rule, and you therefore will need to exercise particular care not to exceed boundaries for their use on different assignments in this class.
Office Hours
Both instructors will have regular office hours. My office hours are Thursdays, 8:30-9:45am in Rice 507.
In addition to regular office hours, we’re happy to meet at other times if you can’t get your questions answered then or if those times don’t work for you. Please don’t hesitate to reach out to set up individual meetings.
You should also feel free to send questions to us via email. We reserve the right to post the question and response (minus any information that would identify who asked the question) to the entire class if doing so will be helpful to others. The same applies to questions asked in other forums such as office hours.
It is the University’s long-standing policy and practice to reasonably accommodate students so that they do not experience an adverse academic consequence when serious personal issues conflict with academic requirements. There are many valid reasons for accommodations and family obligations, personal crises, and extraordinary opportunities are all be potentially valid reasons for accommodations. Due to the nature of this class, though, it is important that you let your instructor know as far in advance as possible so we can discuss alternatives.
Below is an outline of the course with rough topic plans. Because this is a rapidly changing field and evolving course, the topics are expected to change during the course and we will confirm the readings the week before each class and post updated information as the course progresses. We will also be arranging some guest speakers, and will adjust the course schedule around their availability.
Class 1 (28 August): Introduction (Cyberspace and the Law of the Horse, Introduction to LLMs)
Class 2 (4 September): How LLMs Work, Prompt Injection
Class 3 (11 September): Fairness and Discrimination
Class 4 (18 September): Interpretability and Explainability
Class 5 (25 September): Regulatory Approaches
Class 6 (2 October): Midterm
After Class 5, we will cover a range of topics in AI and Law, such as copyright, antitrust, liability and AI safety, privacy, and the use of AI in legal practice. We will also invite outside speakers, so exact topics and dates will depend in part on speaker scheduling, in part on student interest, and in part on developments over the course of the semester.
Final Project Presentations: Scheduled with instructors during end of semester and exam period.