The deployment of systems of artificial intelligence (AI) in high-risk settings warrants the
need for trustworthy AI. This crucial requirement is highlighted by recent EU guidelines and …

Explainable AI (XAI) has been investigated for decades and, together with AI itself, has
witnessed unprecedented growth in recent years. Among various approaches to XAI …

Chatbots are conversational software applications designed to interact dialectically with
users for a plethora of different purposes. Surprisingly, these colloquial agents have only …

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

A significant challenge for recommender systems (RSs), and in fact for AI systems in
general, is the systematic definition of explanations for outputs in such a way that both the …

Recent years witnessed a number of proposals for the use of the so-called interpretable
models in specific application domains. These include high-risk, but also safety-critical …

With the commercialization of ChatGPT, generative artificial intelligence (AI) has been
applied almost everywhere in our lives. However, even though generative AI has become a …

Providing explanations based on user reviews in recommender systems (RS) can increase
users' perception of system transparency. While static explanations are dominant, interactive …

As the field of explainable AI (XAI) is maturing, calls for interactive explanations for (the
outputs of) AI models are growing, but the state-of-the-art predominantly focuses on static …