Fairness, Accountability, Transparency, Privacy

Link to module

Evaluated December 2021

This module, Transparency in Decision-Making Interfaces, and Surveillance in the Times of Covid are part of a complete course on data and how it is has been used, how data is used to gain insight, and how data is used to support decisions. This module exposes students to various issues surrounding class and race/ethnicity and it offers technical ways to address some of the questions and concerns surrounding those issues.

This module covers material in Intelligent Systems/Basic Machine Learning and Intelligent Systems/Advanced Machine Learning.

Instructors adopting this module will find that the course is designed to be taken by both those who have experience with computer science and those who do not. There are some small programming projects in this module. As this is “Lab 12” (of 14) from the course, there is a lot of expertise that is expected of the instructor and the students. Some of that expertise is technical—writing the Python needed to generate the results—and some of it is understanding nuances of terms like “fairness” and “privacy.” The module includes links to pertinent resources for both faculty and students to establish that expertise. Faculty may find discussions with faculty from history, sociology, law, or social work to be of assistance in developing this module. The module could be used over a couple of class periods or as a standalone assignment with a single class period for discussion. Instructors teaching an upper-division Artificial Intelligence course, with some flexibility, could align this with the course material.

Assessments of student learning will need to be developed by the instructor. As the module is structured it provides locations where students could be required to write responses to questions that could facilitate class discussion. However, all these elements will need to be created by the instructor.


The evaluation of this module was led by Marty J. Wolf and Emanuelle Burton as part of the Mozilla Foundation Responsible Computer Science Challenge. Patrick Anderson, Judy Goldsmith, Colleen Greer, Darakhshan Mir, Jaye Nias and Evan Peck also made contributions. These works are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.