by Davide Calvaresi, Giovanni Ciatto, Amro Najjar, Reyhan Aydoğan, Leon Van der Torre, Andrea Omicini, and Michael I. Schumacher
Abstract Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis.
by Joris Hulstijn and Leon Van der Torre
Abstract There is a lot of interest in explainable AI [2, 11]. When a system takes decisions that affect people, they can demand an explanation of how the decision was derived, or a justification of why the decision is justified. Note that explanation and justification are related, but not the same [1]. The need for explanation or justification is more pressing, when the system makes legal decisions [3], or when the decision is based on social or ethical norms [5].
