BELIEVING OR NOT IN ALGORITHMS... ? RECRUITERS' PERCEPTIONS AND BEHAVIOR TOWARDS ALGORITHMS DURING RESUME SCREENING

CROIRE OU NE PAS CROIRE LES ALGORITHMES… ? PERCEPTIONS ET COMPORTEMENT DES RECRUTEURS FACE AUX ALGORITHMES LORS DE LA PRE-SELECTION DE CV

Alain Lacroux () and Christelle Martin Lacroux ()
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Alain Lacroux: UP1 EMS - Université Paris 1 Panthéon-Sorbonne - École de Management de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne
Christelle Martin Lacroux: CERAG - Centre d'études et de recherches appliquées à la gestion - UGA - Université Grenoble Alpes

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Abstract: Resume pre-screening assisted by decision support systems integrating artificial intelligence is currently undergoing a strong development in many organizations, raising technical, managerial, legal and ethical issues. This paper aims to better understand the reactions of recruiters when they are confronted with algorithm-based recommendations during the CV screening process. Two major attitudes have been identified in the literature on users' reactions to algorithm-based recommendations: algorithm aversion, which reflects a general distrust and preference for human recommendations; and automation bias, corresponding to an overconfidence in the decisions or recommendations made by algorithmic decision support systems (ADSS). Based on the results obtained in the field of automated decision support, we hypothesize in general that recruiters trust human experts more than algorithmic decision support systems because they distrust algorithms for subjective decisions such as hiring. An experimental study on resume selection was conducted on a sample of professionals (N=1,100) who were asked to review a job offer and then evaluate two fictitious resumes in a 2×2 factorial design with the manipulation of the type of recommendation (no recommendation/algorithmic recommendation/human expert recommendation) and the relevance of recommendations (relevant vs. irrelevant recommendation). Our results support the general hypothesis of preference for human recommendations: recruiters demonstrate a higher level of trust in human expert recommendations compared to algorithmic recommendations. However, we also found that recommendation relevance has an unexpected differential impact on decisions: in the case of an irrelevant algorithmic recommendation, recruiters favored the least relevant resume over the best resume. This discrepancy between attitudes and behaviors suggests a possible automation bias. Our results also show that some specific personality traits (extraversion, neuroticism, and self-confidence) are associated with differential use of algorithmic recommendations.

Keywords: Personnel selection; Artficial Intelligence; Human resource management; Automation biais; Algorithm aversion; recrutement; intelligence artificielle; gestion des ressources humaines (search for similar items in EconPapers)
Date: 2022-10-19
New Economics Papers: this item is included in nep-ain
Note: View the original document on HAL open archive server: https://paris1.hal.science/hal-04095500v1
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Published in 33ème congrès de l'AGRH, AGRH (association francophone de gestion des ressources humaines), Oct 2022, Brest, France

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