1 citations · 1 across the 4 of their papers we have counts for
4 papers
TaCo: Targeted Concept Erasure Prevents Non-Linear Classifiers From Detecting Protected Attributes
Fanny Jourdan, Louis Béthune, Agustin Picard +2
Ensuring fairness in NLP models is crucial, as they often encode sensitive attributes like gender and ethnicity, leading to biased outcomes. Current concept erasure methods attempt…
Are fairness metric scores enough to assess discrimination biases in machine learning?
Fanny Jourdan, Laurent Risser, Jean-Michel Loubes +1
This paper presents novel experiments shedding light on the shortcomings of current metrics for assessing biases of gender discrimination made by machine learning algorithms on tex…
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks
Fanny Jourdan, Agustin Picard, Thomas Fel +3
Transformer architectures are complex and their use in NLP, while it has engendered many successes, makes their interpretability or explainability challenging. Recent debates have…
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?
Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher +2
Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can however be used for applications whic…