Towards Understanding Human-AI Reliance Patterns Through Explanation Styles

As AI is increasingly embedded in human decision-making pipelines, it is necessary to explore the influence of explainable AI (XAI) on both appropriate and inappropriate patterns of human-AI reliance. While various models have been proposed, the impact of explanation style on reliance in the context of these models is under-explored. In particular, there is a lack of research surrounding how different styles of explanations, such as those presented by feature-based and example-based XAI methods, influence how humans interact with and engage with AI-enabled systems through the lens of specific patterns of reliance. We summarise the current literature in this space and conclude by proposing a study and analysis plan that can be used to compare the individual and combined influences of feature-based and example-based explanation styles.