Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …

With the advent of 5G commercialization, the need for more reliable, faster, and intelligent
telecommunication systems is envisaged for the next generation beyond 5G (B5G) radio …

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 …

Abstract Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great
efforts made by the Explainable AI (XAI) community, explanations lack robustness …

Prototypical part neural networks (ProtoPartNNs), namely ProtoPNet and its derivatives, are
an intrinsically interpretable approach to machine learning. Their prototype learning scheme …

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

In our work, we propose the use of Representational Similarity Analysis (RSA) for
explainable AI (XAI) approaches to enhance the reliability of XAI-based decision support …

Machine learning models have shown increased accuracy in classification tasks when the
training process incorporates human perceptual information. However, a challenge in …

Surging interest in deep learning from high-stakes domains has precipitated concern over
the inscrutable nature of black box neural networks. Explainable AI (XAI) research has led to …

Reliable deployment of machine learning models such as neural networks continues to be
challenging due to several limitations. Some of the main shortcomings are the lack of …