Course Description

Data and algorithms are rapidly transforming law enforcement and criminal justice, including how police officers are deployed, how discrimination is detected, and how sentencing, probation, and parole terms are set. Modern computational and statistical methods offer the promise of greater efficiency, equity, and transparency, but their use also raises complex legal, social, and ethical questions. In this course, we analyze recent court decisions, discuss methods from machine learning and game theory, and examine the often subtle relationship between law, public policy, and statistics. The class is centered around several data-intensive projects in criminal justice that students work on in interdisciplinary teams. Students work closely with criminal justice organizations to carry out these projects, with the goal of producing research that impacts policy.

Students with a background in statistics, computer science, law, and/or public policy are encouraged to participate. Enrollment is limited, and project teams will be selected during the first week of class. If you are interested in taking the class, please complete this short application by Tuesday, April 4.

Instructors
Sharad Goel ()
Jongbin Jung (TA) (email)
Ravi Shroff, New York University (email)
Schedule
Mondays @ 1:30 - 4:20 in Y2E2 105.

Each project group is also required to meet weekly with the teaching staff outside of class, at a mutually convenient time.
Evaluation
The course grade is based on a class project that students work on in teams for the duration of the quarter, and on in-class participation (including weekly project presentations).