Investigating the Role of Explainability and AI Literacy in User Compliance
This research addresses the problem of low user compliance with AI systems due to opacity, which is crucial for designers and developers in human-AI interaction domains, though it is incremental in exploring specific factors.
The study investigated how explainable AI (XAI) and AI literacy affect user compliance with AI recommendations, finding that XAI increases compliance but its effectiveness is influenced by AI literacy and mediated by users' mental models.
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.