Course Description

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 strongly encouraged to wear a mask indoors, regardless of vaccination status. This includes any in-person lectures or office hour sessions. 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.

Lecture Attendance

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 projects. The Stanford Honor Code can be found here. The Stanford Honor Code pertaining to CS courses can be found here.

Course Instructor

Course Assistant

Time & Location

Spring Quarter: Apr. 3 - Jun. 7, 2023
Lecture: Monday, Wednesday 1:30 - 2:50 PM

Office Hours & Contact

Office hours: TBD You can find an up-to-date list of times and locations here.
Contact: Course staff can be reached through Ed or email at the address 'cs281-spr2223-staff appropriate symbol'. Please send to the aforementioned address using your `` account to avoid having the email be viewed as spam.

Grade Breakdown

  • Three Homeworks: 8% each, total 24%
  • Three In-class Socio-Tech Analyses: 7% each, total 21%
    • 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%)

Course Discussions

We use Ed for course communication.

Assignment Details

See here for more details concerning assignments.

Course Projects Details

See here for more details concerning the course projects.


What are the pre-requisites?
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

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.