  AI is becoming increasingly common across different domains. However, as
sophisticated AI-based systems are often black-boxed, rendering the
decision-making logic opaque, users find it challenging to comply with their
recommendations. Although researchers are investigating Explainable AI (XAI) to
increase the transparency of the underlying machine learning models, it is
unclear what types of explanations are effective and what other factors
increase compliance. To better understand the interplay of these factors, we
conducted an experiment with 562 participants who were presented with the
recommendations of an AI and two different types of XAI. We find that users'
compliance increases with the introduction of XAI but is also affected by AI
literacy. We also find that the relationships between AI literacy XAI and
users' compliance are mediated by the users' mental model of AI. Our study has
several implications for successfully designing AI-based systems utilizing XAI.
