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Algorithm Aversion in Prosocial Tasks: Evidence from AI-Based Performance Evaluation

Author

Listed:
  • Abel, Martin

    (Bowdoin College)

  • Dawi, Raghad

    (Bowdoin College)

  • Lenk, Tyler

    (Bowdoin College)

  • Singer, Aidan

    (Bowdoin College)

Abstract

How do workers respond when artificial intelligence replaces human judgment in evaluating prosocial work? Partnering with a non-profit addressing food insecurity, we recruit 1,491 U.S. volunteers to write fundraising messages and cross-randomize evaluation by humans versus AI and the presence of performance pay. AI evaluation reduces effort by 11–14 percent among volunteers with low commitment to the cause, while having no effect on those strongly aligned with the mission. Performance pay fails to mitigate these adverse effects. Workers perceive AI as less effective at identifying quality, which appears to be the primary mechanism, and as less fair and transparent than human evaluation. Introducing an AI algorithm that explicitly applies human evaluation criteria does not mitigate these negative effects, suggesting that resistance to AI evaluation reflects deeper skepticism about machines' capacity for subjective judgment.

Suggested Citation

  • Abel, Martin & Dawi, Raghad & Lenk, Tyler & Singer, Aidan, 2026. "Algorithm Aversion in Prosocial Tasks: Evidence from AI-Based Performance Evaluation," IZA Discussion Papers 18678, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp18678
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    References listed on IDEAS

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    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • M54 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Labor Management

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