Fairness and Bias

Algorithmic bias describes systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. Bias can emerge due to many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm.


Fairness and Machine Learning
This book gives a perspective on machine learning that treats fairness as a central concern rather than an afterthought.
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Practical Data Ethics
Course covering disinformation, bias, ethical foundations, privacy & surveillance, silicon valley ecosystem, and algorithmic colonialism.
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Fairness Indicators: Scalable Infrastructure for Fair ML Systems
Algorithms and the datasets on which ML models are trained on also have the ability to reflect or reinforce unfair biases.
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Lessons from the PULSE Model and Discussion
Elements of the recent discussion that happened among AI researchers as a result of bias found in the model associated with the PULSE paper.
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Scikit-Learn compatible toolkit to assess and improve the fairness of machine learning models.
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AI Fairness 360 Open Source Toolkit
A comprehensive set of fairness metrics for datasets and ML models, explanations for metrics, and algorithms to mitigate bias in datasets and models.
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