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

Author

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  • Sanchaita Hazra
  • Marta Serra-Garcia

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.

Suggested Citation

  • Sanchaita Hazra & Marta Serra-Garcia, 2025. "Understanding Trust in AI as an Information Source: Cross-Country Evidence," CESifo Working Paper Series 11954, CESifo.
    • Handle: RePEc:ces:ceswps:_11954
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    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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