Do people rely on ChatGPT more than their peers to detect deepfake news?

This experimental study investigates whether people rely more on ChatGPT (GPT-4) than on their human peers when detecting AI-generated fake news (deepfake news). In multiple rounds of deepfake detection tasks conducted in a laboratory setting, student participants exhibited a greater reliance on ChatGPT compared to their peers. We explored this over-reliance on AI from two perspectives: the weight of advice (WOA) and the decomposition of reliance (DOR) into two stages. Our analysis indicates that reliance on external advice is primarily influenced by the source and quality of the advice, as well as the subjects’ prior beliefs, knowledge, and experience, while the type of news and time spent on tasks have no effect. Additionally, our study indicates a potential sequential mechanism of advice utilization, wherein the advice source affects reliance in both stages—activation and integration—whereas the quality of the advice, along with knowledge and experience, influences only the second stage. Our findings suggest that relying on AI to detect AI may not be detrimental and could, in fact, contribute to a deeper understanding of human-AI interaction and support advancements in AI development during the Generative Artificial Intelligence (GAI) era.