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Human Trust in AI: Evidence from Experimental Economics

Author

Listed:
  • Bernd Irlenbusch

    (University of Cologne)

Abstract

Artificial intelligence increasingly shapes economic decisions, yet its value depends on whether humans rely on it appropriately. This survey selectively reviews experimental economic evidence (2020 – 2026) on trust in AI, with a focus on privacy, transparency, accountability, fairness, and efficiency. The evidence challenges simple accounts of algorithm aversion or algorithm appreciation. Individuals may underuse beneficial AI because of opacity, autonomy concerns, or institutional distrust, but may also over-rely on deficient systems, disclose excessive data, or delegate responsibility strategically. The survey suggests that trust in AI is best understood as calibrated reliance under informational and institutional constraints. Effective governance should structure informational and institutional environments that help humans calibrate reliance on AI to its actual capabilities, limitations, and social consequences.

Suggested Citation

  • Bernd Irlenbusch, 2026. "Human Trust in AI: Evidence from Experimental Economics," ECONtribute Discussion Papers Series 417, University of Bonn and University of Cologne, Germany.
    • Handle: RePEc:ajk:ajkdps:417
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    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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