Human Learning about AI

Bnaya Dreyfuss and Rapha\"el Raux

Papers from arXiv.org

Abstract: We study \emph{Human Projection} (HP): people's tendency to evaluate AI using the same frameworks they use for humans -- treating features such as task difficulty and the reasonableness of mistakes as diagnostic of overall ability. We formalize HP and its consequences for equilibrium adoption, testing its predictions experimentally. First, people project human difficulty onto AI, overestimating performance on human-easy tasks, underestimating it on human-hard ones, and over-updating after easy failures and hard successes -- leading to systematic misspecification when AI performance is jagged rather than human-ordered. Second, HP interprets observed performance through a single ability index, inducing all-or-nothing adoption even when AI outperforms humans on only some tasks; experimentally stripping AI of human-like cues weakens cross-task generalization and reduces over-adoption. Finally, a field experiment with a parenting-advice chatbot shows that less humanly reasonable mistakes cause larger drops in trust and future engagement. Anthropomorphic AI design can amplify HP, misaligning beliefs and distorting adoption.

Date: 2024-06, Revised 2026-05
New Economics Papers: this item is included in nep-ain and nep-hrm
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