Evaluating What Others Say: The Effect of Accuracy Assessment in Shaping Mental Models of AI Systems

Forming accurate mental models that align with the actual behavior of an AI system is critical for successful user experience and interactions. One way to develop mental models is through information shared by other users. However, this social information can be inaccurate and there is a lack of research examining whether inaccurate social information influences the development of accurate mental models. To address this gap, our study investigates the impact of social information accuracy on mental models, as well as whether prompting users to validate the social information can mitigate the impact. We conducted a between-subject experiment with 39 crowdworkers where each participant interacted with our AI system that automates a workflow given a natural language sentence. We compared participants' mental models between those exposed to social information of how the AI system worked, both correct and incorrect, versus those who formed mental models through their own usage of the system. Specifically, we designed three experimental conditions: 1) validation condition that presented the social information followed by an opportunity to validate its accuracy through testing example utterances, 2) social information condition that presented the social information only, without the validation opportunity, and 3) control condition that allowed users to interact with the system without any social information. Our results revealed that the inclusion of the validation process had a positive impact on the development of accurate mental models, especially around the knowledge distribution aspect of mental models. Furthermore, participants were more willing to share comments with others when they had the chance to validate the social information. The impact of inaccurate social information on altering user mental models was found to be non-significant, while 69.23% of participants incorrectly judged the social information accuracy at least once. We discuss the implications of these findings for designing tools that support the validation of social information and thereby improve human-AI interactions.