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 application of Explainable Artificial Intelligence (XAI) in the field of chemistry has
garnered growing interest for its potential to justify the prediction of black-box machine …

Deep learning models like chatGPT exemplify AI success but necessitate a deeper
understanding of trust in critical sectors. Trust can be achieved using counterfactual …

The continuous development of artificial intelligence (AI) theory has propelled this field to
unprecedented heights, owing to the relentless efforts of scholars and researchers. In the …

Convolutional neural networks (CNNs) have achieved impressive results on imbalanced
image data, but they still have difficulty generalizing to minority classes and their decisions …

Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction
strategies can rarely be understood. With recent advances in Explainable Artificial …

The “black-box” nature of artificial intelligence (AI) models has been the source of many
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human
performance across various domains. This presents us with an opportunity to further human …

Abstract Large pre-trained Vision-Language Models (VLMs) have become ubiquitous
foundational components of other models and downstream tasks. Although powerful, our …

The field of eXplainable Artificial Intelligence (XAI) has witnessed significant advancements
in recent years. However, the majority of progress has been concentrated in the domains of …