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Measuring preferences for algorithms — How willing are people to cede control to algorithms?

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  • Ivanova-Stenzel, Radosveta
  • Tolksdorf, Michel

Abstract

We suggest a simple method to elicit individual preferences for algorithms. By altering the monetary incentives for ceding control to the algorithm, the menu-based approach allows for measuring in particular the degree of algorithm aversion. Using an experiment, we elicit preferences for algorithms in an environment with measurable performance accuracy under two conditions — the absence and the presence of information about the algorithm’s performance. Providing such information raises subjects’ willingness to rely on algorithms when ceding control to the algorithm is more costly than trusting in their own assessment. However, algorithms are still underutilized.

Suggested Citation

  • Ivanova-Stenzel, Radosveta & Tolksdorf, Michel, 2024. "Measuring preferences for algorithms — How willing are people to cede control to algorithms?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 112(C).
  • Handle: RePEc:eee:soceco:v:112:y:2024:i:c:s2214804324001071
    DOI: 10.1016/j.socec.2024.102270
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    References listed on IDEAS

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    1. Ben Greiner, 2015. "Subject pool recruitment procedures: organizing experiments with ORSEE," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 114-125, July.
    2. repec:dar:wpaper:138565 is not listed on IDEAS
    3. Dargnies, Marie-Pierre & Hakimov, Rustamdjan & Kübler, Dorothea, 2022. "Aversion to hiring algorithms: Transparency, gender profiling, and self-confidence," Discussion Papers, Research Unit: Market Behavior SP II 2022-202, WZB Berlin Social Science Center.
    4. Markus Jung & Mischa Seiter, 2021. "Towards a better understanding on mitigating algorithm aversion in forecasting: an experimental study," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 32(4), pages 495-516, December.
    5. Steffen Andersen & Glenn Harrison & Morten Lau & E. Rutström, 2009. "Elicitation using multiple price list formats," Experimental Economics, Springer;Economic Science Association, vol. 12(3), pages 365-366, September.
    6. 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.
    7. Filiz, Ibrahim & Judek, Jan René & Lorenz, Marco & Spiwoks, Markus, 2021. "Reducing algorithm aversion through experience," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
    8. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    9. Efendić, Emir & Van de Calseyde, Philippe P.F.M. & Evans, Anthony M., 2020. "Slow response times undermine trust in algorithmic (but not human) predictions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 157(C), pages 103-114.
    10. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    11. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    12. Margarita Leib & Nils Köbis & Rainer Michael Rilke & Marloes Hagens & Bernd Irlenbusch, 2024. "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty," The Economic Journal, Royal Economic Society, vol. 134(658), pages 766-784.
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    More about this item

    Keywords

    Algorithm aversion; Delegation; Experiment; Preferences;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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