Further
Literature
Further
literature
Journal contributions
Journal contributions
Ninareh Mehrabi et al. (2019):
A Survey on Bias and Fairness in Machine Learning
https://doi.org/10.48550/arXiv.1908.09635
Harini Suresh & John V. Guttag (2019):
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
https://doi.org/10.48550/arXiv.1901.10002
Sven Feuerriegel et al. (2020):
Fair AI: Challenges and Opportunities
DOI: 10.1007/s12599-020-00650-3
Sahil Verma & Julia Rubin (2018):
Fairness Definitions Explained
https://doi.org/10.1145/3194770.3194776
Benjamin van Giffen et al. (2022)
Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods
https://doi.org/10.1016/j.jbusres.2022.01.076
Ryan S. Baker & Aaron Hawn (2021):
Algorithmic Bias in Education
https://doi.org/10.1007/s40593-021-00285-9
European Union Agency For Fundamental Rights (2022):
Bias in algorithms – Artificial intelligence and discrimination
https://fra.europa.eu/en/publication/2022/bias-algorithm
Pratik Gajane, Mykola Pechenizkiy (2017):
On Formalizing Fairness in Prediction with Machine Learning
https://doi.org/10.48550/arXiv.1710.03184
Alessandro Castelnovo et al. (2021):
The Zoo of Fairness Metrics in Machine Learning
https://doi.org/10.21203/rs.3.rs-1162350/v1
Tutorials
Tutorials
Alexis Cook & Var Shankar:
Intro to AI Ethics
https://www.kaggle.com/learn/intro-to-ai-ethics
Martin Wattenberg et al. (based on Hardt et al (2016) – Equality of Opportunity in Supervised Learning):):
Attacking discrimination with smarter machine learning
http://research.google.com/bigpicture/attacking-discrimination-in-ml/
Lex Fridmann (MIT 6.S093):
Introduction to Human-Centered Artificial Intelligence AI
https://www.youtube.com/watch?v=bmjamLZ3v8A
Sarah Bird et al. (WSDM’19 Fairness Tutorial):
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned
https://sites.google.com/view/wsdm19-fairness-tutorial