Deep learning (DL) has been widely used in various fields. However, its black-box nature
limits people's understanding and trust in its decision-making process. Therefore, it becomes …

This work studies the evaluation of explaining graph neural networks (GNNs), which is
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …

Attribution methods are a popular class of explainability methods that use heatmaps to
depict the most important areas of an image that drive a model decision. Nevertheless …

The many successes of deep neural networks (DNNs) over the past decade have largely
been driven by computational scale rather than insights from biological intelligence. Here …

A multitude of explainability methods has been described to try to help users better
understand how modern AI systems make decisions. However, most performance metrics …

In recent years, concept-based approaches have emerged as some of the most promising
explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs) …

Explaining predictions of black-box neural networks is crucial when applied to decision-
critical tasks. Thus, attribution maps are commonly used to identify important image regions …

Today's most advanced machine-learning models are hardly scrutable. The key challenge
for explainability methods is to help assisting researchers in opening up these black boxes …

Deep neural networks (DNNs) are increasingly being used as controllers in reactive
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …

Explainable artificial intelligence (XAI) holds significant importance in enhancing the
reliability and transparency of network decision-making. SHapley Additive exPlanations …