Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence

We run an online experiment to study the origins of algorithm aversion. Participants 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 choose whether the hiring decision between themselves and another worker is made by a participant in the role of a manager or by an algorithm. In a second set of experiments, managers choose whether they want to delegate their hiring decisions to the algorithm. When the algorithm does not use workers’ gender to predict their job-task performance and workers know this, they choose the algorithm more often than in the baseline treatment where gender is employed. Feedback to the managers about their performance in hiring the best workers increases their preference for the algorithm relative to the baseline without feedback, because managers are, on average, overconfident. Finally, providing details on how the algorithm works does not increase the preference for the algorithm for workers or for managers. This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection. Funding: D. Kübler acknowledges financial support from the Deutsche Forschungsgemeinschaft [CRC TRR 190], R. Hakimov acknowledges financial support from the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Project 100018_189152], and M.-P. Dargnies acknowledges financial support from the Agence Nationale de la Recherche (ANR JCJC TrustSciTruths). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02774 .

• 저널Management Science · 72(1) · 285–301
• 토픽Auction Theory and Applications · Management Science and Operations Research
• DOI10.1287/mnsc.2022.02774
• 저자Marie-Pierre Dargnies, Rustamdjan Hakimov, Dorothea Kübler