Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

A number of algorithms in the field of artificial intelligence offer poorly interpretable
decisions. To disclose the reasoning behind such algorithms, their output can be explained …

Deep learning models have achieved high performance across different domains, such as
medical decision-making, autonomous vehicles, decision support systems, among many …

Decision trees have long been recognized as models of choice in sensitive applications
where interpretability is of paramount importance. In this paper, we examine the …

The same method that creates adversarial examples (AEs) to fool image-classifiers can be
used to generate counterfactual explanations (CEs) that explain algorithmic decisions. This …

Counterfactual explanations (CEs) are a powerful means for understanding how decisions
made by algorithms can be changed. Researchers have proposed a number of desiderata …

The last decade has witnessed the rise of a black box society where obscure classification
models are adopted by Artificial Intelligence systems (AI). The lack of explanations of how AI …

In eXplainable Artificial Intelligence (XAI), instance-based explanations have gained
importance as a method for illuminating complex models by highlighting differences or …

Given the complexity and lack of transparency in deep neural networks (DNNs), extensive
efforts have been made to make these systems more interpretable or explain their behaviors …

Hypercomplex neural networks are gaining increasing interest in the deep learning
community. The attention directed towards hypercomplex models originates from several …