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Comparing Different Kinds of Influence on an Algorithm in Its Forecasting Process and Their Impact on Algorithm Aversion

Author

Listed:
  • Zulia Gubaydullina

    (Faculty of Management, Social Work and Construction, HAWK University of Applied Sciences and Art, Haarmannplatz 3, D-37603 Holzminden, Germany)

  • Jan René Judek

    (Faculty of Business, Ostfalia University of Applied Sciences, Siegfried-Ehlers-Str. 1, D-38440 Wolfsburg, Germany)

  • Marco Lorenz

    (Faculty of Economic Sciences, Georg August University Göttingen, Platz der Göttinger Sieben 3, D-37073 Göttingen, Germany)

  • Markus Spiwoks

    (Faculty of Business, Ostfalia University of Applied Sciences, Siegfried-Ehlers-Str. 1, D-38440 Wolfsburg, Germany)

Abstract

Although algorithms make more accurate forecasts than humans in many applications, decision-makers often refuse to resort to their use. In an economic experiment, we examine whether the extent of this phenomenon known as algorithm aversion can be reduced by granting decision-makers the possibility to exert an influence on the configuration of the algorithm (an influence on the algorithmic input). In addition, we replicate the study carried out by Dietvorst et al. (2018). This shows that algorithm aversion recedes significantly if the subjects can subsequently change the results of the algorithm—and even if this is only by a small percentage (an influence on the algorithmic output). The present study confirms that algorithm aversion is reduced significantly when there is such a possibility to influence the algorithmic output. However, exerting an influence on the algorithmic input seems to have only a limited ability to reduce algorithm aversion. A limited opportunity to modify the algorithmic output thus reduces algorithm aversion more effectively than having the ability to influence the algorithmic input.

Suggested Citation

  • Zulia Gubaydullina & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2022. "Comparing Different Kinds of Influence on an Algorithm in Its Forecasting Process and Their Impact on Algorithm Aversion," Businesses, MDPI, vol. 2(4), pages 1-23, October.
    • Handle: RePEc:gam:jbusin:v:2:y:2022:i:4:p:29-470:d:943390
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    References listed on IDEAS

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