CS7792 - Fairness and Dynamics of Learning Systems
Special Topics in Machine Learning
Time and Place
First meeting: August 27, 2021
We are now using artificial intelligence and machine learning for making decision in an ever increasing range of settings. On one end of the range are systems that inform life-altering decisions about hiring, loans, and college admission. On the other end are small decisions like recommending a product to a user, but in aggregate these small decisions have a big impact as well by fundamentally shaping markets like content streaming and e-commerce. This implies a responsibility that these systems are fair, unbiased, and lead to societally desirable outcomes. While quantifying and formalizing decision criteria in a computable way holds a lot of promise, we have come to realize that naively applying machine learning to these problems can lead to undesirable outcomes, can be unfair, and can perpetuate existing biases and flaws of the human decision-making processes in use today.
This seminar discusses issues related to the fairness and long-term dynamics of learning systems as an emerging research area in the intersection of machine learning, causal inference, economics, and information retrieval. Topics include fairness criteria for learning, causality and fairness, mitigating bias in data, and joint human-machine decision making. Concepts will be illustrated with applications, especially in search engines and recommender systems.
The prerequisites for the class are: knowledge of machine learning algorithms and their theory, basic probability, basic statistics, and general mathematical maturity.
Enrollment is primarily reserved for PhD students. All others require permission of instructor.
Canvas (CS7792 Canvas page)
Thorsten Joachims (homepage)
Course Format and Grading
This is a 1-credit seminar, where we will explore the foundational and recent literature on the topic. Everybody will take the lead on presenting some papers, and everybody is expected to be part of the discussion.
Grading is S/U only (no letter grade, no audit). Grades will be determined based on quizzes, paper presentations, peer reviewing, and class participation.
For the paper presentations, we will use peer review. This means that you will comment on other students presentations, giving constructive feedback. The quality of your reviewing also becomes a component of your own grade.
To eliminate outlier grades for quizzes and peer reviews, the lowest grade is replaced by the second lowest grade when grades are cumulated at the end of the semester. So, missing one week is no big deal.
To pass the course, you need to get at least half of the cumulative quiz points, half of the presentation points, half of the peer reviewing points, and half of the class participation points.
We will mostly read original research papers, but the following books and tutorials provide entry points for the main topics of the class:
Other sources for general background on machine learning are:
This course follows the Cornell University Code of Academic Integrity. Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student's own work. Collaborations are allowed only if explicitly permitted. Violations of the rules (e.g. cheating, copying, non-approved collaborations) will not be tolerated. Respectful, constructive and inclusive conduct is expected of all class participants.