Making Recruitment More Inclusive: Unfairness Monitoring With A Job Matching Machine-Learning Algorithm

Abstract

For decades human resources management has relied on recruitment processes rooted in self-reports (such as surveys, questionnaires, personality and cognitive tests) and interviews that were, for most of them, lacking scientific rigor and replicability. Here, we introduce an algorithm that matches job offers and workers that not only outperforms the classic recruitment and job matching methods, but that has at its core algorithmic safeguards to prevent (as much as possible) the pitfall of unfairness and discrimination. Our approach to algorithm development is guided by the constant goal of offering a solution at the cutting edge of technology that has at its core a strict policy of being as fair, inclusive and transparent as possible. ACM Reference Format: Sebastien Delecraz, Loukman Eltarr, Martin Becuwe, Henri Bouxin, Nicolas Boutin, and Olivier Oullier. 2022. Making Recruitment More Inclusive: Unfairness Monitoring With A Job Matching Machine-Learning Algorithm. In International Workshop on Equitable Data and Technology (FairWare ’22), May 9, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3524491.3527309