Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management

Sebastien Delecraz, Loukman Eltarr, Olivier Oullier


Abstract
We introduce how the proprietary machine learning algorithms developed by Gojob, an HR Tech company, to match candidates to a job offer are as transparent and explainable as possible to users (i.e., our recruiters) and our clients (e.g. companies looking to fill jobs). We detail how our matching algorithm (which identifies the best candidates for a job offer) controls the fairness of its outcome. We have described the steps we have taken to ensure that the decisions made by our mathematical models not only inform but improve the performance of our recruiters.
Anthology ID:
2022.legal-1.7
Volume:
Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LEGAL
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
38–43
Language:
URL:
https://aclanthology.org/2022.legal-1.7
DOI:
Bibkey:
Cite (ACL):
Sebastien Delecraz, Loukman Eltarr, and Olivier Oullier. 2022. Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management. In Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference, pages 38–43, Marseille, France. European Language Resources Association.
Cite (Informal):
Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management (Delecraz et al., LEGAL 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.legal-1.7.pdf