Foundations of Fairness in ML

Welcome to Foundations of Fairness in ML, offered Fall 2018!

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 5 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.

This course is open to graduate students with background in machine learning and algorithm design. Any undergraduate who wishes to take the course must ask for the instructor’s permission.

Rough Outline of Topics

This class will cover several different perspectives on fairness in machine learning. Topics will include:


All class announcements will be posted on Piazza.

Course Info


Please read the assigned readings before attending lecture and lab.

Fall 2018 schedule

Lectures Readings
Week 1 Aug 20
Lecture 1 (9/20, Mon):
No Reading
No Class (9/22, Wed)
Week 2 Aug 27
Lecture 2 (8/27, Mon)
What assumptions do we make about our world, our data, our goals?
On the (Im)Possibility of Fairness
Fairness through Awareness
Lecture 3, (8/29, Wed)
Legalese and (some very limited) historical context.

Big Data's Disparate Impact
(Optional) The Sociology of Discrimination: Racial Discrimination in Employment, Housing, Credit, and Consumer Markets
Week 3 Sept 3
No Class (9/3, Mon)
Lecture 4, (9/5, Wed)
Individual Fairness

Fairness through Awareness,
(Optional) Fairness in Learning: Classic and Contextual Bandits
Week 4 Sept 10
No Class (9/10, Mon)
Lecture 5, (9/12, Wed)
Group Fairness Measures: An introduction.
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
Week 5 Sept 17
Lecture 6 (9/17, Mon)
: Continuation of Group Fairness Measures.
No Class, (9/19, Wed)
Week 6 Sept 24
Lecture 7 (9/24, Mon)
Interpolating between Individual and Group Fairness Measures
Preventing Fairness Gerrymandering
Lecture 8, (9/26, Wed):
A critique of these ``fairness" constraints
Algorithmic decision making and the cost of fairness
Week 7 Oct 1
Lecture 9 (10/1, Mon):
What's wrong with classification, anyway?
Invisible Mediators of Action: Classification and the Ubiquity of Standards
Lecture 10, (10/3, Wed):
Fair Representations
Learning Fair Representations
Optional, but highly recommended: Learning Adversarially Fair and Transferable Representations
Week 8 Oct 8
No Class (10/8, Mon)
Fall Recess
Lecture 11, (10/11, Wed):
Is this data (un)fair? How should we "fix" it?
Raw Data is an Oxymoron
And a recap of (Optional) The Sociology of Discrimination: Racial Discrimination in Employment, Housing, Credit, and Consumer Markets
and Data preprocessing techniques for classification without discrimination
Week 9 Oct 15
Lecture 12 (10/15, Mon)
: Economics and Discrimination 1
Lecture 13, (10/18, Wed):
: Economics and Discrimination 2
Week 10 Oct 22
Lecture 14, (10/22, Mon):
Lecture 15, (10/24, Wed):
Week 11 Oct 29
Lecture 16, (10/29, Mon):
Lecture 17, (10/31, Wed):
Week 12 Nov 5
Lecture 18 (11/5, Mon):
Lecture 19, (11/7, Wed)
Week 13 Nov 12
Lecture 20 (11/12, Mon):
Lecture 21, (11/15, Wed)
Week 14 Nov 19
Lecture 22 (11/12, Mon):
No Class, Student Recess, (11/19, Wed)
Week 15 Nov 26
Lecture 23 (11/26, Mon):
Lecture 24, (11/28, Wed)
Week 16 (12/3, NIPS week)
Perhaps no class? (12/3, Mon)
No Class, Reading Period (12/5, Wed)
Week 17 (12/10, Final Exams)