Note: For an updated resource, please see

CS 294: Fairness in Machine Learning

UC Berkeley, Fall 2017
Time: Monday and Friday 2:30PM - 3:59PM
Location: Soda 405
Instructor: Moritz Hardt

Grading policy: 50% in class participation, 50% project

Undergraduate enrollment policy: With permission from the instructor only. Email transcript, and description of any relevant prior projects or research experience.

Students will receive access to a slack channel for class related discussions.



This is an intensive graduate seminar on fairness in machine learning. The focus is on understanding and mitigating discrimination based on sensitive characteristics, such as, gender, race, religion, physical ability, and sexual orientation. Recent years have shown that unintended discrimination arises naturally and frequently in the use of machine learning and algorithmic decision making.

We will work systematically towards a technical understanding of this problem mindful of its social and legal context.


Outline (preliminary)

Last updated: August 17, 2017

Sources of unfairness

Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights
The White House. 2016.

Bias in computer systems
Batya Friedman, Helen Nissenbaum. 1996

The Hidden Biases in Big Data
Kate Crawford. 2013.

Big Data’s Disparate Impact
Solon Barocas, Andrew Selbst. 2014.

Blog post: How big data is unfair
Moritz Hardt. 2014

Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan

Statistical measures of discrimination

Sex Bias in Graduate Admissions: Data from Berkeley
P. J. Bickel, E. A. Hammel, J. W. O’Connell. 1975.

Simpson’s paradox
Pearl (Chapter 6)
Tech report

Certifying and removing disparate impact
Michael Feldman, Sorelle Friedler, John Moeller, Carlos Scheidegger, Suresh Venkatasubramanian

Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Nathan Srebro. 2016.

Blog post: Approaching fairness in machine learning
Moritz Hardt. 2016.

COMPAS and criminal justice

Machine Bias
Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, ProPublica
Code review:

COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity
Northpointe Inc.

Fairness in Criminal Justice Risk Assessments: The State of the Art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, Aaron Roth. 2017.

Courts and Predictive Algorithms
Angèle Christin, Alex Rosenblat, and danah boyd. 2015.
Discussion paper

Limitations of mitigating judicial bias with machine learning
Kristian Lum. 2017.

Trade-offs and impossibility results

Classification, Calibration, Precision, Recall

Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
John C. Platt. 1999.

Inherent Trade-Offs in the Fair Determination of Risk Scores
Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan. 2016.

Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova. 2016.

Attacking discrimination with smarter machine learning
An interactive visualization by Martin Wattenberg, Fernanda Viégas, and Moritz Hardt. 2016.

Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, Aziz Huq. 2017.

The problem of Infra-marginality in Outcome Tests for Discrimination
Camelia Simoiu, Sam Corbett-Davies, Sharad Goel. 2017.

Inherent limitations of observational measures

Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Nathan Srebro. 2016.

Beyond observational measures

Causal reasoning

Background reading:
Pearl (Chapter 1–3)
Pearl (Section 4.5.3)

Elements of Causal Inference
Peters, Janzing, Schölkopf

On causal interpretation of race in regressions adjusting for confounding and mediating variables
Tyler J. VanderWeele and Whitney R. Robinson. 2014.

Causal fairness criteria

Counterfactual Fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva. 2017.

Avoiding Discrimination through Causal Reasoning
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf. 2017.

Fair Inference on Outcomes
Razieh Nabi, Ilya Shpitser

Similarity modeling, matching

Fairness Through Awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Rich Zemel. 2012.

On the (im)possibility of fairness
Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian. 2016.

Why propensity scores should not be used
Gary King, Richard Nielson. 2016.

Measurement, sampling

Raw Data is an Oxymoron
Edited by Lisa Gitelman. 2013.

Blog post: What’s the most important thing in Statistics that’s not in the textbooks
Andrew Gelman. 2015.

Deconstructing Statistical Questions
David J. Hand. 1994.

Statistics and the Theory of Measurement
David J. Hand. 1996.

Measurement Theory and Practice: The World Through Quantification
David J. Hand. 2010

Survey Methodology, 2nd Edition
Robert M. Groves, Floyd J. Fowler, Jr., Mick P. Couper, James M. Lepkowski, Eleanor Singer, Roger Tourangeau. 2009

Sampling: Design and Analysis
Sharon L. Lohr
Chapter 7.6, 8, 12, 14, 15

Unsupervised learning

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai. 2016.

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, Kai-Wei Chang. 2017.

Big Data’s Disparate Impact
Solon Barocas, Andrew Selbst. 2014.

It’s Not Privacy, and It’s Not Fair
Cynthia Dwork, Deirdre K. Mulligan. 2013.

The Trouble with Algorithmic Decisions
Tal Zarsky. 2016.

How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem
Amanda Levendowski. 2017.

Background reading

A Theory of Justice
John Rawls

Equality of Opportunity
John E. Roemer

Judea Pearl

Elements of Causal Inference
Peters, Janzing, Schölkopf

Counterfactuals and Causal Inference
Morgan and Winship

Peyton Young

Weapons of Math Destruction
Cathy O’Neil.