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 inherent in AI applications. You will then apply recent techniques to detect and mitigate such
algorithmic biases, along with methods that 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.
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 projects. The Stanford Honor Code can be
found here. The Stanford
Honor Code pertaining to CS courses can be found
here.
AI Tool Policy
You may use generative AI tools such as Co-Pilot and ChatGPT as you would use a human collaborator. Soliciting direct answers or duplicating responses from these tools is not allowed. Any interactions with these tools must be credited as if they were contributions from collaborators. Using generative AI tools to essentially complete any part of an assignment or project is forbidden and will be considered a breach of the honor code. For additional information, see Generative AI Policy Guidance here.
When submitting your assignment to Gradescope, you must attach any chat transcript directly related to your assignment. We will be tolerant for actions taken in good faith. You can either add screenshots of the transcripts at the end of your submission, or attach a separate pdf file. We may later adjust the policy should we find the AI tool usage undermines the educational value.
Lecture Attendance
We require in-person lecture attendance.
Specifically, the two socio-technical analyses dates will include in-class
projects. More broadly, class participation and in-class discussions will count toward the final grade.
Time & Location
Spring Quarter: March 30 - June 3, 2025 Lecture: Monday, Wednesday 1:30 - 2:50 PM Location:
Course staff can be reached through Ed. For personal correspondance, please send a private message on Ed.
Grade Breakdown
Three Homeworks: 13% each, total 33%
Two In-class Socio-Tech Analyses: 6% each, total 12%
In-class activities: 50%
1-page summary: 50%
Exploratory Project: 15%
Final Project: 35%
Proposal: 10%
Progress Report: 20%
Poster Presentation: 20%
Final Report: 50%
Participation: 5%
Grading Policy
Letter grades in this course will be assigned according to the predetermined scale below. Plus/minus grades will be based on a curve within the scale. Grades below C will be assigned based on standard practices.
A: [90%,100%]
B: [80%,90%)
C: [70%,80%)
FAQ
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.
Academic Accomodations
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
visit oae.stanford.edu.
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
appropriate accommodations.
Acknowledgments. HTML taken from various CS courses given at Stanford: cs231n, cs231a, and
cs229.
