%0 Conference Paper %F Oral %T Gender fairness in job recommendation: a case study %+ Centre de Recherche en Économie et Statistique (CREST) %+ Laboratoire Interdisciplinaire des Sciences du Numérique (LISN) %+ TAckling the Underspecified (TAU) %+ Toulouse School of Economics (TSE-R) %+ Laboratoire de mécanique des solides (LMS) %A Bied, Guillaume %A Gaillac, Christophe %A Hoffmann, Morgane %A Nathan, Solal %A Caillou, Philippe %A Crépon, Bruno %A Sebag, Michèle %F Invité %< sans comité de lecture %B AI for HR and Public Employment Services %C Ghent (BE), Belgium %8 2023-02-09 %D 2023 %Z Computer Science [cs]/Artificial Intelligence [cs.AI]Conference papers %X 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. %G English %L hal-04025006 %U https://inria.hal.science/hal-04025006 %~ X %~ ENSMP %~ GENES %~ CNRS %~ INRIA %~ EHESS %~ INRIA-SACLAY %~ ENSAE %~ X-LMS %~ X-DEP %~ X-DEP-MECA %~ ENSMP_LMS %~ PARISTECH %~ CREST %~ INRIA_TEST %~ ENSAI %~ TESTALAIN1 %~ UT1-CAPITOLE %~ CENTRALESUPELEC %~ INRIA2 %~ PSL %~ UNIV-PARIS-SACLAY %~ ENSMP_DEP_MM %~ X-CREST %~ IP_PARIS %~ IP_PARIS_COPIE %~ INRAE %~ ENSMP-PSL %~ UNIVERSITE-PARIS-SACLAY %~ HYAIAI %~ LISN %~ GS-COMPUTER-SCIENCE %~ LISN-AO