About the Course and Prerequisites

This course will study the sources and measures of unfairness stemming from machine learning, as well as possible interventions to alleviate said unfairness. The class will contain a mix of assignments: there will be 3 assignments. Each assignment will contain 3 parts: a programming task, several mathematical questions which will require rigorous proofs to answer, and an analysis/writing component.

Tentative list of topics (a (!) indicates that the topic may not be covered):

Prerequisites: The course will assume a great deal of comfort with introductory machine learning. It will also assume familiarity with concentration results such as Chernoff/Hoeffding.

Class materials

There will not be a textbook for the course. Our discussion will be guided by papers, monographs, and lecture notes that are available online. An incomplete list that will grow:

Discussion Forum and Email Communication

There will be a Slack channel (first day of class). This is your first resource for questions. For private or confidential questions email the instructor.

Grading and Evaluation

Scribe notes should be prepared using the Latex template. The final notes should be turned in within a few days following lecture with the understanding that the majority of the notes should be completed before lecture.

Schedule

  • Feb 24
  • Feb 26
  • March 2
  • Mar 2
  • Mar 4
  • Mar 9
  • Mar 11
  • Mar 16
  • Mar 18