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 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.

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):
Continuation of Data Preprocessing, discussion of evolvoing models.
Lecture 13, (10/18, Wed):
Runaway feedback loops in predictive policing
Week 10 Oct 22
Lecture 14, (10/22, Mon):
Introduction to learning from experts and bandit learning
Lecture 15, (10/24, Wed):
Definitions of fairness in bandit learning settings
Week 11 Oct 29
Lecture 16, (10/29, Mon):
A guest lecture on allocative fairness, notions of fairness in complete information settings.
Lecture 17, (10/31, Wed):
Greedy algorithms: when do they work, when should they work?
Week 12 Nov 5
Lecture 18 (11/5, Mon):
Long-term outcomes of short-term fairness constraints.
Delayed Impact of Fair Machine Learning
Lecture 19, (11/7, Wed)
Economics and Discrimination 1.
An Economic Argument for Affirmative Action.
Week 13 Nov 12
Lecture 20 (11/12, Mon):
Causality and Fairness 1.
Avoiding Discrimination through Causal Reasoning
Lecture 21, (11/15, Wed):
Causality and Fairness 2.
Counterfactual Fairness
Fair Inference on Outcomes
Week 14 Nov 19
Lecture 22 (11/12, Mon):
Statistics and the theory of measurement
Hand, Deconstructing Statistics.
No Class, Student Recess, (11/19, Wed).
Week 15 Nov 26
Lecture 23 (11/26, Mon)

Class Project Presentations
Lecture 24, (11/28, Wed):
Class Project Presentations
Week 16 (12/3, NIPS week)
No class (12/3, Mon)
No Class, Reading Period (12/5, Wed)
Week 17 (12/10, Final Exams)