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Ethics, Fairness, Responsibility, and Privacy in Data Science (DATA 25900) at The University of Chicago

Webpage and Schedule for DATA-25900 Spring'22

Spring 2022

In this course we explore how societal issues of ethics, fairness, responsibility, and privacy affect the data science lifecycle. The data science lifecycle includes data acquisition, cleaning and pre-processing, analysis and use of data (we explore techniques from machine learning, inferential statistics, and causal inference), and communication of data science results. We will consider the steps one must follow to conduct data science tasks responsibly, and will delve into the details of fairness and privacy issues that arise along the way. The course has three components: lectures, programming assignments, and readings. Through the combination of these three, students who complete the course will learn how to conduct data science tasks responsibly, recognize fairness, privacy, and other important implications, and improve their programming and technical skillset.

Course Information

Instructor: Raul Castro Fernandez (

Teaching Assistant (TA): Zhiru Zhu (

Lectures: Tuesday and Thursday 3:30pm–4:50pm (Central Time)

Prerequisites: CMSC/DATA 11900

Office Hours: You can use office hours to discuss any topic we cover in class. Zhiru: 2-3pm Tuesdays (JCL 236). Raul: 1:30pm - 2:30pm Thursdays in JCL 245.

Canvas Site: Go here We use Canvas mostly for announcements. All other relevant information is here.

Offline/Asynchronous discussion: We will use Campuswire for offline discussion. See the expectations on how to use Campuswire below.


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.


How to ask questions and seek help

Before posting on Campuswire:

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).