With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Abstract Explainable Artificial Intelligence (xAI) is an established field with a vibrant
community that has developed a variety of very successful approaches to explain and …

The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …

The evaluation of explanation methods is a research topic that has not yet been explored
deeply, however, since explainability is supposed to strengthen trust in artificial intelligence …

Deep learning has demonstrated remarkable performance in the medical domain, with
accuracy that rivals or even exceeds that of human experts. However, it has a significant …

The lack of transparency of powerful Machine Learning systems paired with their growth in
popularity over the last decade led to the emergence of the eXplainable Artificial Intelligence …

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 …

Accurate physical activity monitoring is essential to understand the impact of physical activity
on one's physical health and overall well-being. However, advances in human activity …

The rise of deep learning in today's applications entailed an increasing need in explaining
the model's decisions beyond prediction performances in order to foster trust and …

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience
because of their ability to capture nonlinear system behavior and extract predictive …