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.
This class will cover several different perspectives on fairness in machine learning. Topics will include:
All class announcements will be posted on Piazza.
Please read the assigned readings before attending lecture and lab.
Fall 2018 schedule
|Week 1 Aug 20|
|Lecture 1 (9/20, Mon):
|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)
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):
| Learning Fair Representations
Optional, but highly recommended: Learning Adversarially Fair and Transferable Representations
|Week 8 Oct 8|
| No Class (10/8, Mon)
| 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)|