Empirical Legal Studies Workshop

Empirical Legal Studies uses data to inform legal and policy debates. Traditional empirical legal scholarship uses methods such as observational studies and experiments to examine the effects of various policies or legal decisions. More recently, advancements in technology have given rise to a new strand of research that uses tools such as machine learning and natural language processing to study legally relevant datasets at a large scale ("Big Data"). This seminar will present a range of topics that highlight current empirical legal scholarship in these areas. A theme of the course will be comparing and contrasting traditional empirical approaches with the techniques emerging from machine learning and big data. During roughly half of the sessions, we will host a guest speaker who will present an ongoing empirical research project. Familiarity with data science or statistics is not required. Special Instructions: You may write a series of short commentaries on the guest speakers' papers, of which there will be four. Students electing this option will be graded on a Mandatory Pass/Restricted Credit/Fail basis and receive 2 units of credit. Alternatively, you may write a single empirical research paper on a legal topic of your choice. This will satisfy the Law School's Research requirement. These papers will be graded on an Honors/Pass/Restricted Credit/Fail basis. Students taking the seminar for R credit can take the seminar for either 2 or 3 units of credit (section 02), depending on the project. After the term begins, students accepted into the course can transfer from section (01) into section (02), which meets the R requirement, with consent of the instructor. There is no formal prerequisite to take this seminar, though students doing the longer research papers typically have some prior training in statistics. Elements used in grading: Attendance, Class Participation, Four commentaries or one research paper.