How Optimal Transport Can Tackle Gender Biases in Multi-Class Neural Network Classifiers for Job Recommendations

How Optimal Transport Can Tackle Gender Biases in Multi-Class Neural Network Classifiers for Job Recommendations

Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can, however, be used in applications that are ranked as High Risk by the European Commission in the AI act—for instance, online job candidate recommendations. W...

Full description

Bibliographic Details
Main Authors: Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher, Jean-Michel Loubes, Laurent Risser
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Algorithms
Subjects:
fairness
algorithmic bias
neural networks
NLP
recommender systems
multi-class classification
Online Access:https://www.mdpi.com/1999-4893/16/3/174

Similar Items

Similar Items