Guillaume BIED

Guillaume BIED
CREST PhD Student
This user account status is Approved


Toward Job Recommendation for All

This paper presents a job recommendation algorithm designed and validated in the context of the French Public Employment Service. The challenges, owing to the confidential data policy, are related wit ...

Bied Guillaume, Nathan Solal, Perennes Elia, Hoffmann Morgane, Caillou Philippe, Crépon Bruno, Gaillac Christophe, Sebag Michèle

CAI 2023 - The 32nd International Joint Conference on Artificial Intelligence, Aug 2023, Macau, China. pp.5906-5914, 2023

Fairness in job recommendations: estimating, explaining, and reducing gender gaps

Algorithmic recommendations of job ads have the potential to reduce frictional unemployment, but raise concerns about fairness due to biases in past data. Our research investigates the issue of algori ...

Bied Guillaume, Gaillac Christophe, Hoffmann Morgane, Caillou Philippe, Crépon Bruno, Nathan Solal, Sebag Michèle

AEQUITAS 2023 - First AEQUITAS Workshop on Fairness and Bias in AI, Oct. 2023, Krakov, Poland, 2023