In this course, students will engage with law as data, using new legal technologies that promise to shift how lawyers practice in coming years, with a particular emphasis on exploring implications for access to justice. The aim is to examine not how the law regulates new legal technologies, but rather how these technologies can or should be used by legal professionals to advance the rights and interests of marginalized groups.
The course will use a hands-on experiential pedagogy. That is, students will engage directly with new legal technologies – including by completing several small coding projects involving legal data analysis. In addition to exploring these technologies, students will critically reflect on their ethical, professional, social, and economic impacts, focusing on implications for low-income and otherwise marginalized groups.
No prior coding experience is required. The course recognizes that students may bring a range of prior skills and knowledge. Both learning and evaluation have been designed to allow students who are beginners to coding and legal data analysis opportunities to successfully explore a new area, while also allowing students who already have relevant technical skills – as well as students who want to push their skillsets further – to take on more advanced projects. As such, participation is weighted heavily and final projects can be completed with limited coding.
The course involves both synchronous and asynchronous components. After an initial synchronous introductory class, the first half of the course will be delivered asynchronously, through online modules and small coding projects. The instructor will be available for online troubleshooting sessions and for other support during the hours notionally set aside for classes in the weeks when modules and small coding projects are completed. Once the initial modules are completed, a synchronous discussion class will be held to explore ethical, professional, social and economic impacts, with some critical readings provided. The second half of the course will involve students working on a final project either individually or in groups (with the course instructor available for troubleshooting), presenting a draft of that project to colleagues for feedback, and finalizing the project.
Synchronous sessions will be delivered in a hybrid (hyflex) format, meaning that students can elect to attend any given synchronous session either in person or remotely via Zoom. Classes will be scheduled in 3-hour blocks.
Topics:
(1) Introduction to Coding & Access to Justice (Module 1: Automating the boring stuff)
(2) Data Gathering & Cleaning (Module 2: Finding legal datasets and creating new ones)
(3) Data Analysis (Module 3: I have some legal data, now what?)
(4) Artificial Intelligence (Module 4: Using generative AI to advance access to justice)
(5) Student Presentations of Draft Projects
Method of Evaluation:
The course will be evaluated in two parts. The first, worth 50% of the student’s grade, is based on participation. That includes completion of 4 small coding projects (27%), attendance and active participation in course discussions (either in synchronous sessions or on the course e-Class)(10%), feedback provided to colleagues on their draft projects (10%), and completion of course evaluations (3%). To facilitate evaluation of participation, at the conclusion of the course, students will submit a brief self-evaluation describing how they participated in the course. The second, worth 50% of the student’s grade, is a larger final project, which can be completed individually or in groups. For this final project, students will be required to submit drafts for feedback from the course instructor, present a work in progress version of their project to the class (10%), and submit the final project (40%).
NOTE: Learning to meet and communicate effectively about deadlines is one of the course objectives. To this end, late penalties will be applicable to all course components. Students in this course -- including students who might be entitled to flexible deadlines in other courses -- should not expect that accommodations will take the form of extensions to deadlines. Students for whom this might pose barriers should meet with the course instructor in advance of enrolling to discuss whether this course is a good fit. Alternatives are available for students interested in learning about the course topic, but who require additional flexibility, including via supervised research papers.