Internet Censorship Course / Book Workshop
The course material will be delivered in two formats: (1) asynchronous preparation; (2) class meetings.
Preparation. Preparation will entail: (1) readings; (2) a video (or sequence of short videos) on technical content, which will be prepared in lieu of a synchronous lecture. The intention of asynchronous videos will be to reserve class meeting time for more interactive activities: discussion, data analysis activities, etc.
Class meetings. The course will meet regularly for class meetings according to the format and schedule specified for your particular offering (frequency and duration will vary by term and format).
Each class meeting will have one of the following activities.
Prerequisites: There are no required prerequisites, although some background in computer networking, network security and privacy, and machine learning will be helpful to understand some of the concepts covered in the class.
A prior course in computer networking is recommended for some background, but not required. Technical pre-requisites will largely be covered in class.
Additionally, the labs will involve basic exploratory data analysis; some experience with Python and Pandas may be useful, though it is not required.
| Component | Weight |
|---|---|
| Group Research Project | 35% |
| Reading Responses | 25% |
| Presentation | 25% |
| Participation | 15% |
Writeups are due before the topic is covered for that week, to allow for others to read, summarize, and comment. (Grading is 0/1.) Important: Reading responses and lab activities will be graded primarily for completion. While we encourage the use of AI tools to assist with research and writing (with proper acknowledgment and explanation of your prompting approach), the key learning objective is understanding the underlying concepts. We reserve the right to test your understanding of reading and lab topics through various means including pop quizzes in class, oral examinations, presentations, or other assessment methods to ensure you have grasped the fundamental concepts.
We understand that sometimes life events occur and that it’s not always possible to meet every deadline. As such, we are willing to accept late assignments according to the following policy:
Excuses with medical documentation are a legitimate exception and will not count against your late period. Any other reasons for lateness—including but not limited to interviews, conferences, etc.—are not legitimate excuses and any resulting lateness will count against your grace period.
All readings will be posted on the schedule page of this webpage. Readings will comprise popular press articles, draft book chapters from Prof. Feamster’s forthcoming book, notes, and published research papers.
You are taking this class to learn. My goal is to teach you new concepts—any attempts to circumvent that deprive you of the process of learning. To that end, you are responsible for doing your own work in this class.
You may talk to the course staff and to other students (past and present) about any assignments in this class. You can work together in discussing how you approached a particular problem, as long as you acknowledge who you worked with.
“Modern” coding indeed involves a lot of copy/paste/adaptation of existing code blocks, and such adaptation is permissible, within reason. If you find approaches and solutions on the Internet to various problems in this class, you are welcome to borrow ideas and approaches (and even copy code snippets, with acknowledgment and links). Please acknowledge all collaborators and sources.
The primary activity that is not permitted it copying a solution verbatim; if you find yourself running code that has been copy/pasted from anywhere, pause and think. Ask the staff if you have questions about this policy.
The University of Chicago has formal policies related to academic honesty and plagiarism. We abide by these standards in this course. Depending on the severity of the offense, you risk being dismissed altogether from the course.
No collaboration is permitted on quizzes or exams. All work submitted for the project must properly cite ideas and work that are not those of the students in the group.
I acknowledge and even expect that you will use large language models (LLMs) and other AI tools for assistance in completing assignments. This is perfectly acceptable and encouraged. However, you are expected to understand the output these tools generate and think critically about their limitations.
AI-assisted research, writing, and coding can improve efficiency, but only if you understand its outputs and can evaluate their validity. Just as you cannot rely on any single source without verification, these tools do you no good if you cannot critically assess, fact-check, understand, evaluate, improve, debug, and build upon their output with your own analysis and insights.
Therefore, you are allowed to use AI tools to help with research, drafting, coding, and analysis as long as you are confident that you understand the material well enough to engage with it critically yourself. This approach reflects the real world, where you will need to know how to use these tools effectively while maintaining your ability to think independently and critically about complex topics, and where you will ultimately be responsible for understanding the software, code, and arguments that you produce.
Given that this course examines censorship and information control—including the very AI systems you may be using—I encourage you to reflect on the implications of your tool use and consider it as part of your learning about these topics.
When using AI tools, please acknowledge their use in your submissions and be prepared to explain and defend any code, solutions, arguments, analysis, or research you submit.
Topics in this course, particularly those that touch on ethics, policy, the law, and society, may touch on topics that challenge your existing thinking, and certain discussions may make you feel uncomfortable or challenged.
To this end, we seek to make this class an inclusive environment, one of mutual respect for others. The University of Chicago is committed to the principles of free expression, and part of my duty as your instructor is to help foster that environment.
To achieve the vibrant intellectual atmosphere we aim for, an environment of respect for each other is of utmost importance. Every person should conduct themselves with integrity, compassion, and thoughtfulness so everyone feels comfortable participating and benefits from a collective learning experience.
Think not only of what you say but how you say it.
Participation and speaking up is critical to the vibrant intellectual environment we all want to create among ourselves. I may not always be clear in my own presentation (and I also make plenty of mistakes), but I will not know that unless you speak up. If you have a question, there’s a good chance half of the class is probably thinking the same thing. Be bold.
Finally, think about when to step back, so that everyone can have a chance to speak. To that end, I typically make a concerted effort to step back from discussions myself—viewing my role as more of a facilitator—so that you can feel free to speak. If I announce (or allude to) my opinion on a matter in front of the class, that may make some of you less eager to share your own viewpoints, which is also a fail.