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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]

Author

Listed:
  • 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)

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.

Suggested Citation

  • Alain Lacroux & Christelle Martin Lacroux, 2022. "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 Recru," Post-Print hal-04095500, HAL.
    • Handle: RePEc:hal:journl:hal-04095500
    Note: View the original document on HAL open archive server: https://paris1.hal.science/hal-04095500
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    References listed on IDEAS

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    1. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    2. Ursula Oberst & Marc De Quintana & Susana Del Cerro & Andrés Chamarro, 2020. "Recruiters prefer expert recommendations over digital hiring algorithm: a choice-based conjoint study in a pre-employment screening scenario," Management Research Review, Emerald Group Publishing Limited, vol. 44(4), pages 625-641, November.
    3. Ursula Oberst & Marc De Quintana & Susana Del Cerro & Andrés Chamarro, 2020. "Recruiters prefer expert recommendations over digital hiring algorithm: a choice-based conjoint study in a pre-employment screening scenario," Management Research Review, Emerald Group Publishing Limited, vol. 44(4), pages 625-641, November.
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    5. Jessica Ochmann & Sandra Zilker & Sven Laumer, 2021. "The Evaluation of the Black Box Problem for AI-Based Recommendations: An Interview-Based Study," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 232-246, Springer.
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