Other Resources

Courses

A Course on Fairness, Accountability and Transparency in Machine Learning, Utah, Fall 2016
CS 294: Fairness in Machine Learning, UC Berkeley, Fall 2017

Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining, KDD 2016
Anti-discrimination Learning: From Association to Causation, IEEE Big Data 2017
Fairness in Machine Learning, NIPS 2017
Defining and Designing Fair Algorithms, ICML 2018
Anti-discrimination Learning: From Association to Causation, KDD 2018
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned, WSDM 2019
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned, WWW 2019
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned, KDD 2019

A comparative study of fairness-enhancing interventions in machine learning
A multidisciplinary survey on discrimination analysis
A survey on bias and fairness in machine learning
A survey on measuring indirect discrimination in machine learning
An Overview of Fairness in Clustering
Fairness Definitions Explained
Fairness in Learning-Based Sequential Decision Algorithms: A Survey
Fairness in learning-based sequential decision algorithms: A survey
Fairness in machine learning: A survey
Fairness-aware machine learning
Machine learning fairness notions: Bridging the gap with real-world applications
Machine learning testing: Survey, landscapes and horizons
Mathematical notions vs. human perception of fairness: A descriptive approach to fairness for machine learning
On formalizing fairness in prediction with machine learning
On the applicability of machine learning fairness notions
On the applicability of ML fairness notions
Survey on Causal-based Machine Learning Fairness Notions
The measure and mismeasure of fairness: A critical review of fair machine learning