Machine learning has become an indispensable tool for creating intelligent applications, accelerating scientific
discoveries, and making better data-driven decisions. Yet, the automation and scaling of such tasks can have
troubling negative societal impacts. Through practical case studies, you will identify issues of fairness,
justice and truth in AI applications. You will then apply recent techniques to detect and mitigate such
algorithmic biases, along with methods to provide more transparency and explainability to state-of-the-art ML
models. Finally, you will derive fundamental formal results on the limits of such techniques, along with
tradeoffs that must be made for their practical application.
Health and Safety Expectation (COVID-19 protocols)
Following Stanford’s policies, everyone is required to wear a mask indoors, regardless of vaccination status.
This includes any in-person lectures or office hour sessions. Individuals may remove face coverings while
speaking. Some community members may have preferences that go beyond the requirements; it is important that we
treat each others' preferences with respect and care. You can find the most current policies on campus masking
requirements on the COVID-19 Health Alerts site
We require in-person lecture attendance.
Specifically, the three socio-technical analyses dates will include in-class
projects. More broadly, class participation and in-class discussions will count toward the final grade.
Stanford Honor Code
Students are free to form study groups and may discuss
homeworks, projects and assignments in groups. However, for
homeworks, each student must write down
the solutions and code from scratch independently, without referring to any written notes from the joint
session. In other words, each student must understand the solution well enough in order to reconstruct it by
themselves. It is an honor code violation to copy, refer to, or look at written or code solutions from a
previous year or any other source, including but not limited to: official solutions from a previous year, solutions posted online,
and solutions you or someone else may have written up in a
previous year, or from any other source. Furthermore, it is an honor code
violation to post your assignment solutions online, such as on
a public git repo. You must explicitly cite any external content, research,
code or other resources you use for your homeworks, analyses or project. The Stanford Honor Code can be
. The Stanford
Honor Code pertaining to CS courses can be found
Time & Location
: Mar. - Jun., 2022
: Tuesday, Thursday 09:45 AM - 11:15 AM
Gates B12, except for three socio-technical analyses dates.
- Socio-Tech Analyses:
- April 14th -- Gates Fujitsu Room (403)
- May 10th -- Gates 415
- May 26th -- Gates Fujitsu Room (403)
Office Hours & Contact
You can find an up-to-date list of times and locations here
Course staff can be reached through Ed or email at the address 'cs281-spr2122-staff appropriate
Please send to the aforementioned address using your `@stanford.edu` account to avoid having the email be
viewed as spam.
- Three Homeworks: 8% each, total 24%
- Three In-class Socio-Tech Analyses: 8% each, total 24%
- In-class activities: 50%
- 1-page summary: 50%
- Participation: 10%
- Course Project: 42%
- Proposal: 10%
- Progress Report: 20%
- Poster Presentation: 20%
- Final Report: 50%
We use Ed
for course communication.
for more details concerning assignments.
Course Project Details
for more details concerning the course project.
What are the pre-requisites?
Basic knowledge about machine learning from at
least one of CS 221, 228, 229 or 230, or equivalent experience.
Proficiency in a programming language: preferably Python.
Can I audit or sit in?
A significant component of this course is in-class
participation and activities. In general, we are very open to sitting-in guests
if you are a member of the Stanford community (registered student,
staff, and/or faculty), but the primary participants will be
registered students. Out of courtesy, we would appreciate that you
first email us or talk to the instructor after the first class you
attend. If the class is too full and we're running out of
space, we would ask that you please allow registered students to attend.
Is there a textbook for this course?
While there is no required textbook, we offer a recommended reading list here
for this course.
If you need an academic accommodation based on a disability, please register with the Office of Accessible
Education (OAE). Professional staff will evaluate your needs, support appropriate and reasonable accommodations,
and prepare an Academic Accommodation Letter for faculty. To get started, or to re-initiate services, please
If you already have an Academic Accommodation Letter, please make a private Ed post. OAE Letters should be sent
to us at the earliest possible opportunity so that the course staff can partner with you and OAE to make the
HTML taken from various CS courses given at Stanford: cs231n