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

Martin Abel (), Raghad Dawi, Tyler Lenk and Aidan Singer
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Martin Abel: Bowdoin College
Raghad Dawi: Bowdoin College
Tyler Lenk: Bowdoin College
Aidan Singer: Bowdoin College

No 18678, IZA Discussion Papers from IZA Network @ LISER

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.

Keywords: algorithm aversion; algorithmic management; artificial intelligence; intrinsic motivation; worker effort (search for similar items in EconPapers)
JEL-codes: J24 M54 (search for similar items in EconPapers)
Date: 2026-05
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