Understanding Trust in AI as an Information Source: Cross-Country Evidence

Sanchaita Hazra and Marta Serra-Garcia

No 11954, CESifo Working Paper Series from CESifo

Abstract: LLMs are emerging as information sources that influence organizational knowledge, though trust in them varies. This paper combines data from a large-scale experiment and the World Values Survey (WVS) to examine the determinants of trust in LLMs. The experiment measures trust in LLM-generated answers to policy-relevant questions among over 2,900 participants across 11 countries. Trust in the LLM is significantly lower in high-income countries-especially among individuals with right-leaning political views and lower educational attainment-compared to low- and middle-income countries. Using large-scale data on trust from the WVS, we show that patterns of trust in the LLM differ from those in generalized trust but closely align with trust in traditional information sources. These findings highlight that comparing trust in LLMs to other forms of societal trust can deepen our understanding of the potential societal impacts of AI.

Keywords: information; generative AI; accuracy; trust; experiment (search for similar items in EconPapers)
JEL-codes: C72 C91 D83 D91 (search for similar items in EconPapers)
Date: 2025
New Economics Papers: this item is included in nep-ain, nep-exp and nep-soc
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_11954

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