Welcome to the homepage for MS&E234, Data Privacy and Ethics (Winter 2020). This course engages with difficult ethical challenges in the modern practice of data science. The three main focuses are data privacy, personalization and targeting algorithms, and online experimentation. The focus on privacy will raise both practical and theoretical considerations. As part of the module on experimentation, students will be required to complete the Stanford IRB training for social and behavioral research. The course will assume a strong familiarity with the practice of machine learning and data science. Strongly recommended: MS&E 226, MS&E 231, CS 229, or equivalents.

The course meets for Mondays lectures and Wednesday discussions, both at 2:30-3:50pm PT.
In Holidays weeks (W2 MLK, W6 President’s Day) lectures will be Wednesday and discussions will be Friday, again both at 2:30-3:50pm PT.

Instructor: Prof. Johan Ugander (MS&E), jugander@
Office hours: (non-Holiday) Mondays 4:45p-5:30p PT
TA: Jenny Hony, jyunhong@
Office Hours:
  • PS1, Due Week 3, Wednesday 1/27, 14:30 PT
    • Fri Jan 22 13:30 – 14:30 PT
    • Mon Jan 25 13:30 – 14:30 PT
  • PS2, Due Week 5, Wednesday 2/10, 14:30 PT
    • Wed Feb 3 13:30 – 14:30 PT
    • Mon Feb 8 13:30 – 14:30 PT
  • PS3, Due Week 7, Wednesday 2/24, 14:30 PT
    • Wed Feb 17 13:30 – 14:30 PT
    • Mon Feb 22 13:30 – 14:30 PT
  • Project:
    • Wed Mar 3 13:30 – 14:30 PT
    • Mon Mar 8 13:30 – 14:30 PT
    • Wed Mar 10 13:30 – 14:30 PT

The course evaluation consists of three parts: problem sets (40%), in-class discussion leading and participation (20%), and group project reports and presentations (40%). Students will rotate to lead Wednesday discussions. There will be 3 problem sets that include significant data manipulation and coding. These will be due Wednesdays of Week 3, Week 5, and Week 7. Group projects will be developed over the course of the quarter and presented during Week 10.

Lectures will be recorded, but synchronous attendance is expected. Please email Prof. Ugander if you will be missing lecture. Discussion section attendance is mandatory. Because of the key role of discussions, is not possible to complete this course asynchronously.

The detailed course readings are given below. This is the fourth time this course is being given (though the first pandemic edition) and it covers very recent topics, so the course content may change slightly as the course evolves. The evaluation criteria will not.

Week 1: Introduction (1/11, 1/13)

Week 2: Digital exhaust and privacy (W 1/20, F 1/22)
Discussion paper: Sweeney (2000)

Week 3: Differential privacy (1/25, 1/27)
Discussion paper: Chaudhuri & Monteleoni (2009)
Week 4: Data transparency, public records, right to be forgotten (2/1, 2/3)
Discussion paper: Bertram et al. (2019)
Week 5: A/B testing, experimentation (2/8, 2/10)
Discussion paper: Kramer et al. (2014)
Week 6: Search engines and recommendation systems (W 2/17, F 2/19)
Discussion paper: White & Horvitz (2015)
Week 7: Personalization and Fingerprinting (2/22, 2/24)
Discussion paper: Englehardt & Narayanan (2016).
Week 8: Social networks, social data (3/1, 3/3)

Discussion paper: Kosinski et al. (2013)

Week 9: Privacy Regulation (3/8, 3/10)
Discussion paper: Goodman & Flaxman (2016)
Week 10: Presentations (3/15, 3/17)
  • Presentations by students.
Students with Documented Disabilities
Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty dated in the current quarter in which the request is made. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. For more information: http://studentaffairs.stanford.edu/oae