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 …

The field of explainable artificial intelligence (XAI) is gaining increasing importance in recent
years. As a consequence, several surveys have been published to explore the current state …

We present a method to explain the decisions of black box models for time series
classification. The explanation consists of factual and counterfactual shapelet-based rules …

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 …

An interesting case of the well-known Dataset Shift Problem is the classification of
Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The …

A central issue addressed by the rapidly growing research area of eXplainable Artificial
Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine …

The growing availability of time series data has increased the usage of classifiers for this
data type. Unfortunately, state-of-the-art time series classifiers are black-box models and …

Machine learning (ML) systems are affected by a pervasive lack of transparency. The
eXplainable Artificial Intelligence (XAI) research area addresses this problem and the …

Machine Learning (ML) has revolutionized various domains, offering predictive capabilities
in several areas. However, with the increasing accessibility of ML tools, many practitioners …

In this chapter, we describe how to generate not only interpretable but also self-explaining
fuzzy systems. Such systems are expected to manage information granules naturally as …