COMM 106E: Data, Science, and Society (Fall 2022, UCSD)
Note: This is a draft syllabus, some details may be subject to change.
- COMM 106E: Data, Science, and Society
- Profs: Stuart Geiger (Communication/HDSI)
- Time: M/W 3:30-4:50pm
- Place: MCC 201
- Course management system: UCSD Canvas (https://canvas.ucsd.edu). All enrolled students should have access to this class on Canvas.
Today, data science methods are being combined with unprecedented levels of personal data collection, producing artificial intelligences that replace human judgment: Who should be hired for a particular job? Who should get admitted to a selective school? Who is considered a suspicious threat for law enforcement? How much is a house worth? How fast will a disease spread if we implement different measures? What social media posts violate a platform’s ‘community standards’? What posts or stories should show up on a personalized news feed? What e-mails should be filtered out of inboxes as ‘spam’? Can we automatically detect and remove “fake news”?
This class focuses on how data science, statistics, computer programming, and artificial intelligence are being delegated important social decisions, and then what the rest of society ought to do in response to these developments. This is a non-traditional course in which students will learn both the societal and technical aspects of these issues. Prof. Geiger is jointly appointed between Communication and Data Science, and this course is similarly an experimental mashup of these two quite different disciplines. This course is explicitly designed for students from Communication and related majors who have no or little experience with programming, statistics, or data analysis, but who want to gain some familiarity with these ways of knowing in a safe and welcoming space. Students with data science skills are also welcome!
Tuesdays will be a more Communication-style lecture and discussion, followed by a Thursday “lab” where we will be learning how to analyze data in a way that is related to the topic discussed on Tuesday. For example, we will learn about the many problems that arise in college rankings, then make our own college rankings based on what each of us think is most important. We will learn how bias and discrimination gets built into machine learning classifiers, then audit a real-world system used to moderate social media posts.
The goal is for students to be able to critically engage with these developments in a hands-on way, as well as have a strong foundation to take further classes in programming and data science. We will be curious and critical about the potentials and perils of these approaches, as we connect issues of data and science to issues of power, political economy, labor, inequality, privacy, and more. The assessments will mostly involve reading, reflecting, and writing about these topics like in most COMM classes. However, students must also complete weekly lab assignments and an open-ended final “data-based” project of their own design. Students will have to learn or understand some math concepts, but no more than is expected in Algebra II (no Calculus will be needed).