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 …

Explainable artificial intelligence (XAI) methods shed light on the predictions of machine
learning algorithms. Several different approaches exist and have already been applied in …

Visualisation techniques are powerful tools to understand the behaviour of Artificial
Intelligence (AI) systems. They can be used to identify important features contributing to the …

Modern deep learning methods have long been considered black boxes due to the lack of
insights into their decision-making process. However, recent advances in explainable …

One of the unsolved challenges in the field of Explainable AI (XAI) is determining how to
most reliably estimate the quality of an explanation method in the absence of ground truth …

To make advanced learning machines such as Deep Neural Networks (DNNs) more
transparent in decision making, explainable AI (XAI) aims to provide interpretations of DNNs' …

The leap in performance in state-of-the-art computer vision methods is attributed to the
development of deep neural networks. However it often comes at a computational price …

Deep Neural Networks (DNNs) excel at learning complex abstractions within their internal
representations. However, the concepts they learn remain opaque, a problem that becomes …

In the rapidly evolving landscape of deep learning (DL), understanding the inner workings of
neural networks remains a significant challenge. This need for transparency and …

In an era of information abundance and visual saturation, the need for resources to organise
and access the vast expanse of visual data is paramount. Abstract concepts-such as comfort …