Do People Trust Humans More Than ChatGPT?

Abstract

We explore whether people trust the accuracy of statements produced by large language models (LLMs) versus those written by humans. While LLMs have showcased impressive capabilities in generating text, concerns have been raised regarding the potential for misinformation, bias, or false responses. In this experiment, participants rate the accuracy of statements under different information conditions. Participants who are not explicitly informed of authorship tend to trust statements they believe are human-written more than those attributed to ChatGPT. However, when informed about authorship, participants show equal skepticism towards both human and AI writers. There is an increase in the rate of costly fact-checking by participants who are explicitly informed. These outcomes suggest that trust in AI-generated content is context-dependent.

Keywords: Artificial intelligence, machine learning, trust, belief

JEL Classification: O33, C91, D8