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ORCIDFanny Jourdan
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2020 – today
- 2024
[b1]Fanny Jourdan:
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). University of Toulouse, France, 2024- [i5]Fanny Jourdan:
Advancing Fairness in Natural Language Processing: From Traditional Methods to Explainability. CoRR abs/2410.12511 (2024) - 2023
- [j1]Fanny Jourdan
, Titon Tshiongo Kaninku, Nicholas Asher, Jean-Michel Loubes, Laurent Risser:
How Optimal Transport Can Tackle Gender Biases in Multi-Class Neural Network Classifiers for Job Recommendations. Algorithms 16(3): 174 (2023) - [c3]Fanny Jourdan, Agustin Martin Picard, Thomas Fel, Laurent Risser, Jean-Michel Loubes, Nicholas Asher:
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP. ACL (Findings) 2023: 5120-5136 - [c2]Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher, Jean-Michel Loubes, Laurent Risser:
Breaking Bias: How Optimal Transport Can Help to Tackle Gender Biases in NLP Based Job Recommendation Systems? EWAF 2023 - [c1]Fanny Jourdan, Ronan Pons, Nicholas Asher, Jean-Michel Loubes, Laurent Risser:
Is a Fairness Metric Score Enough to Assess Discrimination Biases in Machine Learning? EWAF 2023 - [i4]Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher, Jean-Michel Loubes, Laurent Risser:
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations? CoRR abs/2302.14063 (2023) - [i3]Fanny Jourdan, Agustin Martin Picard, Thomas Fel, Laurent Risser, Jean-Michel Loubes, Nicholas Asher:
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks. CoRR abs/2305.06754 (2023) - [i2]Fanny Jourdan, Laurent Risser, Jean-Michel Loubes, Nicholas Asher:
Are fairness metric scores enough to assess discrimination biases in machine learning? CoRR abs/2306.05307 (2023) - [i1]Fanny Jourdan, Louis Béthune, Agustin Picard, Laurent Risser, Nicholas Asher:
TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability. CoRR abs/2312.06499 (2023)
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