Spring 2021

This course takes a technical approach to exploring societal issues of ethics, fairness, responsibility, and privacy related to the collection, use, and generalization of data. The course introduces fundamental techniques related to data acquisition, data cleaning, sampling, statistical modeling, experimental design, feature engineering, and modeling with machine learning. It then explores the problems that arise in different ways of performing those tasks, the fairness and bias of machine learning models, data visualizations, and user interfaces. In addition, the course covers anonymization and deanonymization, conceptions of privacy from a number of perspectives (statistical, legal, and philosophical), and compliance with contractual or legal requirements around data. The course concludes by discussing current controversies around the use and misuse of data. Through both programming assignments and discussions, students who complete the course will learn how to design systems that are inclusive and respectful of all data subjects.

Course Information

Instructor: Raul Castro Fernandez (raulcf@uchicago.edu)

Teaching Assistant (TA): Zhiru Zhu (zhiru@uchicago.edu)

Lectures: Tuesday and Thursday 2:40pm–4:00pm (Central Time)

Prerequisites: CMSC 11900

Office Hours: You can use office hours to discuss any topic we cover in class. I expect most students will use office hours to discuss their individual projects. Times will be available soon.

Canvas Site: Go here

Zoom: The Zoom link we will use for the class is available through the Canvas site indicated above.

Offline discussion: We will use Campuswire for offline discussion. You can think of Campuswire as Piazza or Ed. If you need to discuss a private matter then send me an email instead.

Schedule

The schedule is available here.

The schedule includes lecture topics, readings, as well as the dates where all assignments will be released and due. Make sure to check the schedule often, as I expect there will be some adjustments to the dates throughout the quarter.

Grading

• Programming Assignments (4 in total. 7% each. Total 28%). The programming assignments are an important component of your grade. You can find the tentative release dates in the schedule. You can work on these assignments in pairs and you need to indicate how did you collaborate on each of the assignments.
• Programming Assignments Quiz (5%) A quiz towards the end of the quarter with questions about the programming assignments. This will be easy if you’ve done the work on the programming assignments throughout the quarter.
• Reading Responses (6 in total. 2% each. Total 12%) We will assign readings each week. At the end of the week we will ask a question about the readings and ask you to provide a brief answer.
• Individual Project (40%) A big component of your grade. You will engage in a quarter-long individual project. You will deliver a short report and present your work to the class during the two final sessions.
• Responsibility/Ethics/Privacy Issue Report (12%) In addition to the above, you will deliver a short (maximum 450 words) report where you detail an issue related to the topics covered in class throughout the quarter. In particular, the report should indicate the problem you’ve identified. Some valid topics include news in the media, books, articles, and others. If you are unsure about the topic, make sure you reach out to the instructor soon in the quarter to confirm. In addition to pinpointing the problem, this report should explain clearly why it’s a problem, and what are some fixes or measures that one could take to ameliorate or avoid this issue in the future.
• Class Participation (3%). A small part of your grade comes from participation. If your grade ends up on a border between two grades (e.g. B+ and A-) this can sway your grade. Participation can be earned in several ways: i) being active on Campuswire, i.e., answering and commenting on questions (asking questions on Campuswire does not count) ii) actively engaging in discussion and asking questions in class.

Academic Integrity Policy

The University of Chicago has formal policies related to academic honesty and plagiarism, as described by the university broadly and the college specifically. We abide by these standards in this course. Depending on the severity of the offense, you risk being dismissed altogether from the course. All cases will be referred to the Dean of Students office, which may impose further penalties, including suspension and expulsion. If you have any question about whether some activity would constitute cheating, please feel free to ask. In addition, we expect all students to treat everyone else in the course with respect, following the norms of proper behavior by members of the University of Chicago community.

Student interactions are an important and useful means to master course material. We recommend that you discuss the material in this class with other students. While it is acceptable to discuss assignments in general terms, it is not acceptable to turn in someone else’s writing or code (or fragments thereof) as your own. When the time comes to write down your answer, you should write it down yourself from your own understanding. Moreover, you should cite any material discussions or written sources, e.g., “Note: I discussed this exercise with Jane Smith.” If one student “helps” another by giving them a copy of their assignment, only to have that other student copy it and turn it in, both students are culpable. If you have any questions about what is or is not proper academic conduct, please ask an instructor. (This description of academic honesty is derived in part from those of Stuart Kurtz and John Reppy).

UChicago Health Pact

All students on campus are required to adhere to the guidelines in the UChicago Health Pact in order to promote a safe environment in the classroom.

• Secure face coverings must be worn appropriately at all times at all times while in University buildings

• Maintain a distance of 6 feet from others

• Do not attend and in-person class if you feel unwell or are experiencing COVID-19 related symptoms

The complete text of the UChicago Health Pact along with additional information about COVID-19 protocols can be found here.

Any concerns over inappropriate PPE usage, physical distancing, cleaning/disinfection, or other COVID-19 related public health concerns should be directed to UCAIR. If there is an emergency, call 773-702-8181 or dial 123 on any campus phone.

If you were potentially exposed to COVID-19 or your COVID-19 test results come back positive, reach out immediately to C19HealthReport@uchicago.edu.