Law, Order, and Algorithms

Data and algorithms are rapidly transforming law enforcement and the criminal legal system, 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 examine the often subtle relationship between law, public policy, and technology, drawing on recent court decisions, and applying methods from machine learning and game theory. We survey the legal and ethical principles for assessing the equity of algorithms, describe computational techniques for designing fairer systems, and consider how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises, though prior programming experience is not necessary.


Sharad Goel [ email ]
Office Hours: Tuesdays @ 10am - 12pm, and by appointment

TF: Damarcus Bell [ email ]
Office Hours: Sundays @ 5 - 6pm via Zoom (Zoom link on Canvas)


Mondays and Wednesdays @ 12-1:15pm in Littauer Bldg 280

On-time attendance at lectures is required. Our aim is to create a collaborative and supportive learning environment. One of the best ways to learn the course material is to engage with the lectures by asking questions. If you need to miss a class or will be late, please email Sharad prior to the lecture.

Please do not use electronics (laptops, tablets, phones) during the lecture and discussion components of class. However, please do bring your laptops to every class session, as there will often be in-class coding and analysis exercises. If it helps you follow along with the lectures, consider printing the slides ahead of time; if you do this, it often works best to print 9 slides per page. See here and here on why we institute a no-electronics policy during lectures and discussions. We’re happy to make exceptions in special circumstances.


It is our intent that all students, regardless of their backgrounds or perspectives, be well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength, and benefit. We aim to present materials and conduct activities in ways that are respectful of this diversity. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students. You may use our (anonymous) comment box to let us know which aspects of the class are going well and which could be improved.


Grades are based on class participation and a final project. Specifically, the various components of the course are weighted as follows:

Final projects

Final projects should be conducted in teams of 3-5 students. The expectation is to complete a research paper (approximately 10 single-spaced pages) and presentation (approximately 10-15 minutes) that: (a) describes the emerging use of algorithms in a new domain and analyzes the potential benefits and harms; (b) analyzes the legal and policy implications of new uses of algorithms (e.g., whether current anti-discrimination law will need to adjust to these developments); and/or (c) develops computational methods for assessing the equity of algorithms in a given domain setting. These projects are opportunities for you to examine a wider range of techniques, domains, and/or legal principles than we are able to cover in the class lectures.

To help formulate your final project, please sign up to discuss your idea with Sharad during the fourth week of the semester. After that meeting, complete a project proposal by Monday, September 27. To facilitate project ideation and team matching, you can use this document to brainstorm and express your interest in different topics.

In-class presentations will be held on Monday, November 29 and Wednesday, December 1 (sign-up sheet), and final papers are due on Monday, December 6.

[ Tentative ] Syllabus

The slides and labs below integrate material from past iterations of this course that I have taught over the last several years. They were developed in collaboration with many people, including: Alex Chohlas-Wood, Madison Coots, Josh Grossman, Daniel E. Ho, Jongbin Jung, Jerry Lin, Julian Nyarko, Cheryl Phillips, Hao Sheng, Amy Shoemaker, Ravi Shroff, Sabina Tomkins, and Keniel Yao.