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
Tutorials
- 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
Workshops and Conferences
- Discrimination and Privacy-Aware Data Mining (DPADM), ICDM 2012
- FAT/ML, since 2014
- Privacy and Discrimination in Data Mining, ICDM 2016
- Machine Learning and the Law, NIPS 2016
- FATREC, since 2017
- Data & Algorithm Bias (DAB 2017), 2017
- AIES, since 2018
- FAT* Conference, since 2018
- FATES, since 2019
Datasets
Surveys
- 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