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Obtaining Fairness and Removal of Algorithmic Bias in Machine Learning using Optimal Transport Theory

Leurquin, Bryan
(2021)

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Leurquin_39391400_2021.pdf
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Details

Supervisors
Segers, Johan
Faculty
Faculté des sciences
Degree label
Master [120] en statistique, orientation générale, à finalité spécialisée
Abstract
Machine learning is an ever evolving tool ever more used in many fields such as science, medical or legal. All these are fields in which an unwarranted difference in treatment between two individuals could have disastrous consequences. Datasets can harbor discriminatory behavior and their use in machine learning can lead to unfair treatment of individuals. This paper discusses how unfairness can be quantified and how bias can be reduced with the use of data repair procedures based on optimal transport theory.

Citations

  • APA

Leurquin, B. (2021). Obtaining Fairness and Removal of Algorithmic Bias in Machine Learning using Optimal Transport Theory. https://dial-mem.test.bib.ucl.ac.be/handle/2078.2/24799