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 Tuesday lectures in Thornton 110 and Thursday discussions in Thornton 211, both at 1:30-2:50pm.

Instructor: Prof. Johan Ugander (MS&E), jugander@
Office hours: Thursdays 10:30-11:30
TA: Jan Overgoor, overgoor@
Office hours:
  • PS1, Due Thursday 1/23, 13:30
    Week 2 – Thu Jan 16 12:30-13:30 (Huang 3rd floor)
    Week 3 – Tue Jan 21 12:30-13:30 (Hangout)
  • PS2, Due Thursday 2/6, 13:30
    Week 4 – Thu Jan 30 12:30-13:30 (Huang B007)
    Week 5 – Tue Feb 04 12:30-13:30 (Hangout)
  • PS3, Due Thursday 2/20, 13:30
    Week 6 – Thu Feb 13 12:30-13:30 (Huang B007)
    Week 7 – Tue Feb 18 12:30-13:30 (Hangout)
    Week 7 – Thu Feb 20 12:30-13:30 (Huang B007)
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 Thursday discussions. There will be 3 problem sets that include significant data manipulation and coding. These will be due Thursday of Week 3, Week 5, and Week 7. Group projects will be developed over the course of the quarter and presented during Week 10.
The detailed course readings are given below. This is the third time this course is being given 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 (1/7, 1/9)

Week 2: Digital exhaust and privacy (1/14, 1/16)
Discussion paper: Sweeney (2000)

Week 3: Differential privacy (1/21, 1/23)
Discussion paper: Chaudhuri & Monteleoni (2009)
Week 4: Data transparency, public records, right to be forgotten (1/28, 1/30)
Discussion paper: Bertram et al. (2019)
Week 5: A/B testing, experimentation (2/4, 2/6)
Discussion paper: Kramer et al. (2014)
Week 6: Search engines and recommendation systems (2/11, 2/13)
Discussion paper: White & Horvitz (2015)
Week 7: Personalization and Fingerprinting (2/18, 2/20)
Discussion paper: Englehardt & Narayanan (2016).
Week 8: Social networks, social data (2/25, 2/27)

Discussion paper: Kosinski et al. (2013)

Week 9: Privacy Regulation (3/3, 3/5)

Discussion paper: Goodman & Flaxman (2016)

Week 10: Presentations (3/10, 3/12)
  • 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