Abstract Explainability in Artificial Intelligence (AI) has been revived as a topic of active
research by the need of conveying safety and trust to users in the “how” and “why” of …

The contemporary process-aware information systems possess the capabilities to record the
activities generated during the process execution. To leverage these process specific fine …

This tutorial extensively covers the definitions, nuances, challenges, and requirements for
the design of interpretable and explainable machine learning models and systems in …

Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on
developers, users, and the general public to identify fairness problems and make …

Calls for heightened consideration of fairness and accountability in algorithmically-informed
public decisions-like taxation, justice, and child protection-are now commonplace. How …

Deep models that are both effective and explainable are desirable in many settings; prior
explainable models have been unimodal, offering either image-based visualization of …

Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the
recent years, Argumentation has been used for providing Explainability to AI. Argumentation …

In recent years, there has been an increased interest in more user-centered evaluation
metrics for recommender systems such as those mentioned in [49]. It has also been …

The interest in explainable artificial intelligence has grown strongly in recent years because
of the need to convey safety and trust in the 'how'and 'why'of automated decision-making to …

A study of the role of abductive inference in everyday argumentation and legal evidence
Examines three areas in which abductive reasoning is especially important: medicine …