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Project
A network embedding approach to a fair, efficient and fulfilling job market.
Information
Project Team
Outputs and Outcomes
Publications
FAIRRET : a framework for differentiable fairness regularization terms
Maarten Buyl
Marybeth Defrance
Tijl De Bie
C1
Conference
2024
Inherent limitations of AI fairness
Maarten Buyl
Tijl De Bie
A1
Journal Article
in
COMMUNICATIONS OF THE ACM
2024
Fairness regularization in machine learning : methods and limitations
Maarten Buyl
Tijl De Bie
Jefrey Lijffijt
Dissertation
2023
RankFormer : listwise learning-to-rank using listwide labels
Maarten Buyl
Paul Missault
Pierre-Antoine Sondag
P1
Conference
2023
Optimal transport of classifiers to fairness
Maarten Buyl
Tijl De Bie
C1
Conference
2022
Tackling algorithmic disability discrimination in the hiring process : an ethical, legal and technical analysis
Maarten Buyl
Christina Cociancig
Cristina Frattone
Nele Roekens
C1
Conference
2022