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Understanding the Source of Algorithmic Aversion: An Experimental Approach

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  • Elia Antoniou

Abstract

This paper examines the source of algorithmic aversion, defined as the unwillingness to accept advice or decisions made by algorithms. Algorithmic aversion is conceptualized as a form of individual partiality, driven either by belief-based factors attributed to differences in perceived ability, or by preference-based factors which reflect a disamenity associated with selecting algorithms. To empirically test the predictions of the model, a preregistered online experiment was conducted, where participants evaluated answers to objective and subjective economic questions, with varying information on whether the source was human or algorithm. The results provide no evidence of algorithmic aversion in the evaluation task: participants did not systematically favor human-generated answers over algorithm-generated ones. These results suggest that algorithmic aversion may not be as robust or uniform as previously assumed.

Suggested Citation

  • Elia Antoniou, 2025. "Understanding the Source of Algorithmic Aversion: An Experimental Approach," University of Cyprus Working Papers in Economics 05-2025, University of Cyprus Department of Economics.
    • Handle: RePEc:ucy:cypeua:05-2025
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    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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