CSE 599, Winter 2020
Foundations of Fairness in Machine Learning

Lecture: Mon, Wed 3-4:20pm Room: COURSE WILL MEET IN ROOM CSE2 387 (GATES CENTER)

Instructor office hours: TBD

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):

Definitions of Fairness

Statistical vs Individual definitions of fairness

Sources of Unfairness in ML

Tensions between different fairness notions

Fairness interventions and long-term effects of those interventions

Equilibria: does competition “drive out” any unfairness a business’s model might have?

Unfair data vs unfair objectives or algorithms

Thinking outside the (CS) box: what do other communities think unfairness means, and how have they grappled with these questions?

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

(30%) 3 homeworks each will have 1 programming task, 2-3 problems requiring proofs, and 1 paper to read/write a critique, refutation, or supporting argument for.

(30%) In-class discussion Each class will have readings, there will be several questions during lecture related to the readings. Contribute to some of these discussions

(40%) Final Project This project can be empirical (awesome!) or formal or some combination thereof. Can be done solo or in pairs.

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

Jan. 6

Welcome, logistics, overview of course topics

Jan. 8

What assumptions do we make about our world, our data, our goals?

Reading: On the (Im)Possibility of Fairness, Lecture notes
, Fairness through Awareness

Jan. 13

Legalese and (some very limited) historical context.

Reading: Big Data's Disparate Impact
(Optional) The Sociology of Discrimination: Racial Discrimination in Employment, Housing, Credit, and Consumer Markets

Jan. 15

Individual Fairness

Reading: Fairness through Awareness

Jan. 20

No Class (MLK day)

Reading:

Jan. 22

Statistical Notions of Fairness

Reading: Equality of Opportunity in Supervised Learning
(Optional/Recommended) Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
(Optional/Recommended) Trade-Offs in the Fair Determination of Risk Scores

Jan. 27

Virtual Class, part 1.

Reading: Please read 1 paper appearing at fatconference.org for 2019 or 2020, and outline the primary perspective/strengths/weaknesses of said approach. Please post this to slack by midnight on Tuesday Jan 28. Look for the thread the instructor will set up to post your summary.

Jan 29

Virtual Class, part 2.

Please comment on at least 2 of your classmates' summaries from Tuesday, with either questions, additional related research, or another discussion point regarding the research direction.