Harvard CS 2881R
Everyone who wants to be considered for the course must fill out the Fall 2026 course registration form.
In-person attendance will be mandatory for students enrolled in the course.
Read Homework Zero HW0 is due August 5, 2026, at 11:59 p.m. Eastern Time.
Submitting Homework Zero is a necessary but not sufficient condition for admission to the course. Completing the assignment does not guarantee admission.
To get a sense of the issues we will cover, please read It’s 2030 and we fucked up. How did it happen? and All Watched Over.
Fall 2026, Thursdays 3:45pm–6:30pm Eastern Time (first lecture September 3)
Classroom: Room 2112, 114 Western Avenue, Allston, MA 02134 (building information and directions)
Instructor: Boaz Barak
Teaching Fellows: Natalia Siwek, Terry Zhou, Michael Shoemate, Ege Çakar, Itay Lavie, Lia Zheng, and Hugh Van Deventer
Course email: cs2881@boazbarak.org
| Links for registered students only: Canvas | Perusall |
Course Description: This is a graduate-level course on challenges in the alignment and safety of artificial intelligence. We will consider technical questions as well as societal and other impacts of the field.
Prerequisites: We require mathematical maturity and proficiency with proofs, probability, and information theory, along with the foundations of machine learning at the level of an undergraduate course such as Harvard CS 181 or MIT 6.036. On the applied side, students should be comfortable programming in Python and training a basic neural network.
The course will have 13 in-person lectures. Each lecture will also involve discussion and the presentation of an experiment by a group of students.
Attendance: In-person attendance is mandatory. Students are expected to attend all lectures, do the reading in advance, and discuss the readings in an electronic forum.
AI use: Students are allowed and encouraged to use generative and agentic AI as much as they can for studying, exploring concepts, and completing their assignments and projects. We will explore ways to either give students access to compute credits or reimburse compute expenses.
Electronic device policy: Students can use laptops in class, but we will ask those using them to sit in the back so they do not distract other students.
Assignments: The course will involve the following assignments: presenting an experiment in class, writing scribe notes, completing a final project, and potentially completing one or more homework assignments or mini-projects. Grading will be decided later.
Lecture recordings: To the extent technically possible, we intend to record and publish the lectures online, though there might be some delay in doing so. Note that recording is done automatically by a static in-room camera, and some parts of the lecture (e.g., whiteboard work or discussions) may not be captured as well. In addition, we will honor requests by external speakers not to record their talks.
POTENTIAL CONFLICT OF INTEREST NOTE: In addition to his position at Harvard, Boaz is also a member of the technical staff at OpenAI. The course will include discussions of models from multiple providers, including OpenAI, and students are also encouraged to use AIs from multiple providers while doing their work. If students in the course have any concerns about this conflict, please do not hesitate to contact Boaz, the other staff, or the Harvard SEAS administration. For what it is worth, I (Boaz) will see it as a great success of the course if its graduates work in AI safety in any capacity, including in academia, nonprofits, governments, or any of OpenAI’s competitors.
Additional lecture topics and materials will be added as they are confirmed.
Unless otherwise noted, all lectures meet in person on Thursdays from 3:45pm–6:30pm Eastern Time in Room 2112, 114 Western Avenue, Allston, MA 02134.
Lecture materials:
Required pre-reading and viewing:
Optional but recommended:
Registered students should access the readings through Perusall and contribute substantive comments or replies to the discussion.
Guest lecturer: Nicholas Carlini (Anthropic, virtual)
Required pre-reading:
Optional reading:
Registered students should access the required readings through Perusall and contribute substantive comments or replies to the discussion by noon Eastern Time on Thursday, September 10, 2026.
| Previous versions: Fall 2025 AI Safety | Spring 2023 ML Theory Seminar | Spring 2021 ML Theory Seminar |