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

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  • Marie-Pierre Dargnies
  • Rustamdjan Hakimov
  • Dorothea Kübler

Abstract

We run an online experiment to study the origins of algorithm aversion. Participants are either in the role of workers or of 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 either 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. In the baseline treatments, we observe that workers choose the manager more often than the algorithm, and managers also prefer to make the hiring decisions themselves rather than delegate them 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. Providing details on how the algorithm works does not increase the preference for the algorithm, neither for workers nor for managers. Providing feedback to managers about their performance in hiring the best workers increases their preference for the algorithm, as managers are, on average, overconfident.

Suggested Citation

  • Marie-Pierre Dargnies & Rustamdjan Hakimov & Dorothea Kübler, 2022. "Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence," CESifo Working Paper Series 9968, CESifo.
  • Handle: RePEc:ces:ceswps:_9968
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    References listed on IDEAS

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    Cited by:

    1. Ivanova-Stenzel, Radosveta & Tolksdorf, Michel, 2023. "Measuring Preferences for Algorithms - Are people really algorithm averse after seeing the algorithm perform?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277692, Verein für Socialpolitik / German Economic Association.
    2. Mathieu Chevrier & Brice Corgnet & Eric Guerci & Julie Rosaz, 2024. "Algorithm Credulity: Human and Algorithmic Advice in Prediction Experiments," GREDEG Working Papers 2024-03, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    3. Mallory Avery & Andreas Leibbrandt & Joseph Vecci, 2023. "Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech," Monash Economics Working Papers 2023-09, Monash University, Department of Economics.
    4. In'acio B'o & Li Chen & Rustamdjan Hakimov, 2023. "Strategic Responses to Personalized Pricing and Demand for Privacy: An Experiment," Papers 2304.11415, arXiv.org.

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    More about this item

    Keywords

    algorithm aversion; experiment; hiring discrimination; transparency;
    All these keywords.

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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