Beneficial Mistrust in Generative AI? The Role of AI Literacy in Handling Bad Advice

Dirk Leffrang (), Nina Passlack (), Oliver Müller () and Oliver Posegga ()
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Dirk Leffrang: Paderborn University
Nina Passlack: University of Bamberg
Oliver Müller: Paderborn University
Oliver Posegga: University of Bamberg

No 136, Working Papers Dissertations from Paderborn University, Faculty of Business Administration and Economics

Abstract: Despite the increasing proliferation of Generative Artificial Intelligence (GenAI), systems like large language models (LLMs) can sometimes present misleading or false information as true – a problem known as "hallucinations." As GenAI systems become more widespread and accessible to the general public, understanding how AI literacy influences advice-taking from imperfect GenAI advice is crucial. Drawing on the correspondence bias, we study how individuals with varying AI literacy levels react to GenAI providing bad advice. Gathering empirical evidence through an online programming experiment, we find that AI-literate individuals take less advice, especially while receiving bad advice, but not exclusively. We outline how correspondence bias can explain these variations, reconciling mixed findings of prior studies on AI literacy. Our research thus contributes a holistic perspective on the beneficial and detrimental mistrust through AI literacy to education, integration, and evaluation programs of AI, highlighting the dangers of naive evaluation strategies.

Keywords: AI literacy; artificial intelligence; AI education; algorithm aversion (search for similar items in EconPapers)
JEL-codes: C91 D83 D91 O33 (search for similar items in EconPapers)
Pages: 33
Date: 2025-04
New Economics Papers: this item is included in nep-ain and nep-exp
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