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

This paper proposes a formal top-level model of explaining the outputs of machine-learning-
based decision-making applications and evaluates it experimentally with three data sets …

Today's AI landscape is permeated by plentiful data and dominated by powerful data-centric
methods with the potential to impact a wide range of human sectors. Yet, in some settings …

Explaining outputs determined algorithmically by machines is one of the most pressing and
studied problems in Artificial Intelligence (AI) nowadays, but the equally pressing problem of …

An autonomous system is constructed by a manufacturer, operates in a society subject to
norms and laws, and is interacting with end-users. We address the challenge of how the …

Computational models of legal precedent-based reasoning developed in AI and Law are
typically based on the simplifying assumption that the background set of precedent cases is …

We develop a notion of explanations for acceptance of arguments in an abstract
argumentation framework. To this end we show that extensions returned by Dung's standard …

As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and
emulate abstract reasoning. Studies in early MR have notably started inquiries into …

Ethical and explainable artificial intelligence is an interdisciplinary research area involving
computer science, philosophy, logic, and social sciences, etc. For an ethical autonomous …

Neural Networks (NNs) are often referred to as “black box” models, which has sparked
increasing interest among researchers in understanding their internal workings …