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. Students work in interdisciplinary
teams to explore these issues in an empirical or investigative project of their choice.
Prerequisites: An introductory course in applied statistics (e.g., MS&E 125). Experience programming in R or Python is encouraged but not required.
We're offering a crash course on R on Saturday, January 9 and Sunday, January 10. If you're interested, please sign up here. You can view the R course materials here.
Maya Perelman (email)
Ravi Shroff, Visiting Scholar, New York University (email)
In teams of 2-5 people, write a 2-3 page proposal for your final project. Clearly state and motivate your research question, list potential data sources, and outline a tentative methodology. Before submitting your proposal, discuss the feasibility and appropriateness of the project with Sharad and/or Ravi. Office hours are a good time to do this, but we can also schedule alternative times to meet. The final project consists of a report (approximately 10 single-spaced pages) and an in-class presentation. Please submit your proposal here.
Report Due Date: Thursday, March 17, 11:59 pm PT (submit)
In-class presentations should be approximately 15 minutes, with an additional 5 minutes for questions. Your final paper should clearly state and motivate your research question, summarize the related literature, describe your methods, detail your results (and include the appropriate plots), and discuss the implications of your findings. The paper should be approximately 10 single-spaced pages long (please submit a PDF file).