Human Trust in AI: Evidence from Experimental Economics

Bernd Irlenbusch ()
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Bernd Irlenbusch: University of Cologne

No 417, ECONtribute Discussion Papers Series from University of Bonn and University of Cologne, Germany

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.

Keywords: Trust in AI; calibrated reliance; algorithm aversion; algorithm appreciation; privacy; transparency; accountability; fairness; efficiency (search for similar items in EconPapers)
JEL-codes: C90 C91 C92 C93 O33 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2026-06
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https://www.econtribute.de/RePEc/ajk/ajkdps/ECONtribute_417_2026.pdf First version, 2026 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:ajk:ajkdps:417

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