Simon Fraser University
Engaging the World

Course Outlines

Fall 2025 - CMPT 419 D200

Special Topics in Artificial Intelligence (3)

Collective Action and Public Interest AI

Class Number: 5523

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 3 – Dec 2, 2025: Mon, 2:30–3:20 p.m.
    Burnaby

    Sep 3 – Dec 2, 2025: Thu, 2:30–4:20 p.m.
    Burnaby

  • Exam Times + Location:

    Dec 12, 2025
    Fri, 3:30–6:30 p.m.
    Burnaby

Description

CALENDAR DESCRIPTION:

Current topics in artificial intelligence depending on faculty and student interest.

COURSE DETAILS:

Special Topics in AI, Collective Action and Public Interest AI

Artificial intelligence (AI) technologies have seen a large surge in interest from researchers, investors, businesses, and everyday end-users. These technologies stand on the shoulders of giants -- they rely on a large body of research in computing and other fields, as well as modern feats of engineering from organizations that operate them. However, they also rely heavily on data, and thus, people.

Search engines rely on click data from users and content written by volunteers such as blogs and Wikipedia articles. Recommender systems rely on explicit user feedback (e.g., “star ratings”) and behavioral data (e.g., browsing history) that reveal user preferences. Supervised learning relies on crowd workers, volunteers, and sometimes unwitting users (e.g., reCAPTCHA participants) to label images and text. And new generative AI systems rely on the wide swathe of content shared on the web. Without this data generated by the public, technologies that use machine learning and statistical models could not exist. The critical role of data suggests an untapped source of power for data creators, i.e., the broad public. Furthermore, it suggests a number of exciting questions about how a data-centric view can advance both AI research and the development of AI products and other systems.

In this course, we will explore AI technologies with a data-centric, and thus human-centric lens. We will discuss topics such as:
- Exposure to foundational reading in interdisciplinary AI
- The intersection of humanities scholarship and technical computing aspects of AI
- Modern research in data valuation
- Relevant work in social computing, including the impact of online platform design choices.
- The potential for collective action involving data. How might social movements -- ranging from protests that withhold data to movements to collect and share data in the public interest -- impact the future of AI?
- The economics of data. Students will be introduced to recent work on data markets and unique properties of buying/selling data for AI.

We will read papers on these topics together. Students will work together to synthesize and present knowledge from research papers, and present their own opinions on these topics. The course will centre a structured final project that will enable students to conduct interdisciplinary responsible AI research or bring responsible AI concepts to bear in industry contexts.

Students may benefit from having taken a course in AI, ML, or data science (or have equivalent experience from e.g. an internship, a research project, a personal project).

Example SFU courses:
CMPT 310 - Intro Artificial Intelligence
CMPT 353 - Computational Data Science
CMPT 414 - Computer Vision

Having taken an HCI course or relevant social science course (e.g., sociology, economics) is a plus, but CS students without this experience who want to explore interdisciplinary CS work that is “human-centered” are welcome. Similarly, students in the humanities who have some exposure to data science are also welcome.

We will work through a low-stakes "example assignment" in week 1 so that students can assess their comfort level.

In short, students should ideally be decently comfortable with both (1) working with computational notebooks (Python, R, Julia, etc.), quickly loading and working with quantitative data, training and evaluating machine learning models and (2) reading and critically thinking about new scholarly perspectives and ethical considerations.

This course will include a heavy reading component.

COURSE-LEVEL EDUCATIONAL GOALS:


The course will aim to develop the following skills:
- students will become more comfortable reading research papers that take an interdisciplinary approach to study AI
- students will gain experience presenting information from papers
- there will be a project component that incorporates coding
- students will be able to articulate some of the ongoing challenges in “human-centric” AI.

Students will gain exposure to the following concepts:
- interdisciplinary research in AI
- data valuation techniques, and their applications for AI research/practice
- social computing

Grading

NOTES:

Readings, assignments, and class structure will be discussed in class. Readings will involve a mix of research papers and other materials.

The grading scheme will be discussed in the first week of class.

REQUIREMENTS:

See Course Details (above) for comments on suggested background.

Materials

MATERIALS + SUPPLIES:

Materials will be provided by the instructor. We will read an assortment of research papers, book chapters, and other public materials.

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Department Undergraduate Notes:

The following are default policies in the School of Computing Science. Please check your course syllabus whether the instructor has chosen a different policy for your class, otherwise the following policies apply.
 
  • Students must attain an overall passing grade on the weighted average of exams in the course in order to get a C- or higher.
  • All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes, or no later than one week after a student adds a course. After considering a request, an instructor may provide a concession or may decline to do so. Students requiring accommodations as a result of a disability can contact the Centre for Accessible Learning (caladmin@sfu.ca).

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.