Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …

The large and still increasing popularity of deep learning clashes with a major limit of neural
network architectures, that consists in their lack of capability in providing human …

Placing a human in the loop may help abate the risks of deploying AI systems in safety-
critical settings (eg, a clinician working with a medical AI system). However, mitigating risks …

Attention is a mechanism that has been instrumental in driving remarkable performance
gains of deep neural network models in a host of visual, NLP and multimodal tasks. One …

Deep neural networks have demonstrated promising performance on image recognition
tasks. However, they may heavily rely on confounding factors, using irrelevant artifacts or …

Deep learning methods are highly accurate, yet their opaque decision process prevents
them from earning full human trust. Concept-based models aim to address this issue by …

Graph neural networks (GNNs) are highly effective on a variety of graph-related tasks;
however, they lack interpretability and transparency. Current explainability approaches are …

Recently, in healthcare organizations, real-time data have been collected from connected or
implantable sensors, layered protocol stacks, lightweight communication frameworks, and …

The field of explainable artificial intelligence emerged in response to the growing need for
more transparent and reliable models. However, using raw features to provide explanations …

The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing
graph network explainers attempt to address this issue by providing post-hoc explanations …