[Ml-stat-talks] Fairness in ML Course Fall 2017

Barbara Engelhardt bee at princeton.edu
Wed Sep 13 10:21:30 EDT 2017

Hi All,

I wanted to let you know about a course offered this fall on Fairness in


>From the instructor, Arvind Narayanan:

*You might be interested in a new grad seminar on fairness in machine
learning: https://registrar.princeton.edu/course-offerings/course_details.xml?courseid=002126&term=1182

*Machine learning discovers and reproduces patterns in existing data. Thus,
unthinking application of machine learning risks perpetuating societal
biases including racial and gender bias. This seminar studies the emerging
science of fairness in machine learning. Readings cover: sources of bias in
machine learning; methods for detecting, measuring, and mitigating bias;
individual fairness, group fairness, and the tension between them;
connections between fairness and privacy; bias in algorithmic decision
making; bias in unsupervised machine learning. Students take on hands-on
empirical projects of their choosing.*

*Readings are drawn primarily from AI/ML/stats, supplemented by a few
readings from fields like social science, law, and policy. Here's a sample
reading list:*

*M. Hardt, E. Price, and N. Srebro. "Equality of opportunity in supervised
learning." Advances in Neural Information Processing Systems (NIPS), 2016.*
*J. Kleinberg, S. Mullainathan, and M. Raghavan. “Inherent Trade-Offs in
the Fair Determination of Risk Scores.” Innovations in Theoretical Computer
Science (ITCS), 2017.*
*S. Barocas, and A. D. Selbst. "Big data's disparate impact." California
Law Review, 2016.*
*A. Caliskan, J. J. Bryson, and A. Narayanan. "Semantics derived
automatically from language corpora contain human-like biases." Science,
*A. Datta, M. C. Tschantz, and A. Datta. "Automated experiments on ad
privacy settings." Proceedings on Privacy Enhancing Technologies (PETS),

Barbara E Engelhardt
Assistant Professor
Department of Computer Science
Center for Statistics and Machine Learning
Princeton University
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