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Conference Papers Year : 2023

Gender fairness in job recommendation: a case study

Abstract

Algorithmic recommendations of job ads to job seekers promise to alleviate frictional unemployment, but raise fairness considerations due to biases in training data. This paper strives to discuss the issue of algorithmic fairness, with a focus on gender, in a hybrid job recommender system trained on past hires developed in partnership with the French Public Employment Service.
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Dates and versions

hal-04025006 , version 1 (11-03-2023)

Identifiers

  • HAL Id : hal-04025006 , version 1

Cite

Guillaume Bied, Christophe Gaillac, Morgane Hoffmann, Solal Nathan, Philippe Caillou, et al.. Gender fairness in job recommendation: a case study. AI for HR and Public Employment Services, Feb 2023, Ghent (BE), Belgium. ⟨hal-04025006⟩
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