Investigating the Role of Explainability and AI Literacy in User Compliance

TL;DR

This study explores how explainability and AI literacy influence user compliance with AI recommendations, highlighting the mediating role of mental models in understanding and trust.

Contribution

It provides empirical evidence on the effects of different types of XAI and AI literacy on user compliance, emphasizing the importance of mental models.

Findings

XAI increases user compliance

AI literacy positively influences compliance

Mental models mediate the relationship between XAI, literacy, and compliance

Abstract

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.