Fanny Jourdan

According to our database, Fanny Jourdan authored at least 14 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Revisiting Anisotropy in Language Transformers: The Geometry of Learning Dynamics.

Raphael Bernas, Fanny Jourdan, Antonin Poché, Céline Hudelot

CoRR, April, 2026

2025

Interpreto: An Explainability Library for Transformers.

Antonin Poché, Thomas Mullor, Gabriele Sarti, Frédéric Boisnard, Corentin Friedrich, Charlotte Claye, François Hoofd, Raphael Bernas, Céline Hudelot, Fanny Jourdan

CoRR, December, 2025

EuroBERT: Scaling Multilingual Encoders for European Languages.

Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves, André F. T. Martins, Ayoub Hammal, Caio Corro, Céline Hudelot, Emmanuel Malherbe, Etienne Malaboeuf, Fanny Jourdan, Gabriel Hautreux, João Alves, Kevin El Haddad, Manuel Faysse, Maxime Peyrard, Nuno Miguel Guerreiro, Patrick Fernandes, Ricardo Rei, Pierre Colombo

CoRR, March, 2025

FairTranslate: an English-French Dataset for Gender Bias Evaluation in Machine Translation by Overcoming Gender Binarity.

Fanny Jourdan, Yannick Chevalier, Cécile Favre

Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025

ConSim: Measuring Concept-Based Explanations' Effectiveness with Automated Simulatability.

Antonin Poché, Alon Jacovi, Agustin Martin Picard, Victor Boutin, Fanny Jourdan

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Advancing fairness in natural language processing : from traditional methods to explainability. (L'équité dans le traitement automatique des langues : des méthodes traditionnelles vers l'explicabilité).

Fanny Jourdan

PhD thesis, 2024

Advancing Fairness in Natural Language Processing: From Traditional Methods to Explainability.

Fanny Jourdan

CoRR, 2024

2023

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

Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher, Jean-Michel Loubes, Laurent Risser

Algorithms, March, 2023

TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability.

Fanny Jourdan, Louis Béthune, Agustin M. Picard, Laurent Risser, Nicholas Asher

CoRR, 2023

Are fairness metric scores enough to assess discrimination biases in machine learning?

Fanny Jourdan, Laurent Risser, Jean-Michel Loubes, Nicholas Asher

CoRR, 2023

COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks.

Fanny Jourdan, Agustin Martin Picard, Thomas Fel, Laurent Risser, Jean-Michel Loubes, Nicholas Asher

CoRR, 2023

Is a Fairness Metric Score Enough to Assess Discrimination Biases in Machine Learning?

Fanny Jourdan, Ronan Pons, Nicholas Asher, Jean-Michel Loubes, Laurent Risser

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

Breaking Bias: How Optimal Transport Can Help to Tackle Gender Biases in NLP Based Job Recommendation Systems?

Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher, Jean-Michel Loubes, Laurent Risser

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP.

Fanny Jourdan, Agustin Martin Picard, Thomas Fel, Laurent Risser, Jean-Michel Loubes, Nicholas Asher

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023