The rise in deep neural networks (DNNs) has led to increased interest in explaining their
predictions. While many methods for this exist, there is currently no consensus on how to …

Hybrid Intelligence is an emerging concept that emphasizes the complementary nature of
human intelligence and artificial intelligence (AI). One key requirement for collaboration …

Prior work has identified a resilient phenomenon that threatens the performance of human-
AI decision-making teams: overreliance, when people agree with an AI, even when it is …

This paper contributes to the growing literature in empirical evaluation of explainable AI
(XAI) methods by presenting a comparison on the effects of a set of established XAI methods …

We investigate whether post-hoc model explanations are effective for diagnosing model
errors--model debugging. In response to the challenge of explaining a model's prediction, a …

Structural data well exists in Web applications, such as social networks in social media,
citation networks in academic websites, and threads data in online forums. Due to the …

We investigate whether three types of post hoc model explanations–feature attribution,
concept activation, and training point ranking–are effective for detecting a model's reliance …

Neural networks have been shown to be extremely effective rainfall-runoff models, where
the river discharge is predicted from meteorological inputs. However, the question remains …

Explaining the decisions of an Artificial Intelligence (AI) model is increasingly critical in many
real-world, high-stake applications. Hundreds of papers have either proposed new feature …

Explaining artificial intelligence (AI) predictions is increasingly important and even
imperative in many high-stake applications where humans are the ultimate decision-makers …