Aversion to hiring algorithms: Transparency, gender profiling, and self-confidence

Marie-Pierre Dargnies, Rustamdjan Hakimov and Dorothea Kübler
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Marie-Pierre Dargnies: DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique

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Abstract: We run an online experiment to study the origins of algorithm aversion. Partici-pants are in the role of either workers or managers. Workers perform three real-effort tasks:task 1, task 2, and the job task, which is a combination of tasks 1 and 2. They choosewhether the hiring decision between themselves and another worker is made by a partici-pant in the role of a manager or by an algorithm. In a second set of experiments, managerschoose whether they want to delegate their hiring decisions to the algorithm. When thealgorithm does not use workers' gender to predict their job-task performance and workersknow this, they choose the algorithm more often than in the baseline treatment where gen-der is employed. Feedback to the managers about their performance in hiring the bestworkers increases their preference for the algorithm relative to the baseline without feed-back, because managers are, on average, overconfident. Finally, providing details on howthe algorithm works does not increase the preference for the algorithm for workers or formanagers.

Keywords: artificial intelligence; economics: behavior and behavioral decision making; economics: microeconomic behavior (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

Published in Management Science, 2024, ⟨10.1287/mnsc.2022.02774⟩

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Related works:
Working Paper: Aversion to hiring algorithms: Transparency, gender profiling, and self-confidence (2023)
Working Paper: Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence (2022)
Working Paper: Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence (2022)
Working Paper: Aversion to hiring algorithms: Transparency, gender profiling, and self-confidence (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04662073

DOI: 10.1287/mnsc.2022.02774

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