FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes

FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes

With the increased use of machine learning algorithms to make decisions which impact people’s lives, it is of extreme importance to ensure that predictions do not prejudice subgroups of the population with respect to sensitive attributes such as race or gender. Discrimination occurs when...

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Bibliographic Details
Main Authors: Teresa Salazar, Miriam Seoane Santos, Helder Araujo, Pedro Henriques Abreu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Classification bias
fairness
imbalanced data
K-nearest neighborhood
oversampling
Online Access:https://ieeexplore.ieee.org/document/9442706/

Internet

https://ieeexplore.ieee.org/document/9442706/

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