Overview

Description

CALENDAR DESCRIPTION:

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

COURSE DETAILS:

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:
- Modern research in data valuation. Students will be asked to think about ways we might understand AI capabilities in terms of different possible datasets
- The economics of data. Students will be introduced to recent work on data markets and unique properties of buying/selling data for AI.
- The platforms where data is generated. Students will be introduced to 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?

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.

This course will accommodate both undergraduate and graduate students.

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 students without this experience who want to explore interdisciplinary CS work that is “human-centered” are welcome.

This course will include a heavy reading component.

COURSE-LEVEL EDUCATIONAL GOALS:

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

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

This course will involve a heavy reading and writing load.

Students will be able to articulate some of the ongoing challenges in “human-centric” AI.

Grading

Materials

MATERIALS + SUPPLIES:

Materials will be provided by the instructor. There will be no primary reference material -- rather, we will read an assortment of research papers, book chapters, etc.

REQUIRED READING NOTES: