Increasingly, laws are being proposed and passed by governments around the world to
regulate artificial intelligence (AI) systems implemented into the public and private sectors …

Our research aims to propose a new performance-explainability analytical framework to
assess and benchmark machine learning methods. The framework details a set of …

This paper presents a model for the progressive visualization and exploration of the
structure of large datasets. That is, an abstraction on different components and relations …

Beyond their differences, most black-box explanation methods share a number of features
and can be framed in a common structure. We identify these features and propose a generic …

Abstract Machine learning is becoming increasingly important to control the behavior of
safety and financially critical components in sophisticated environments, where the inability …

This paper works towards an initial ontology of assessment techniques for building AI-
enriched human-centered XR systems, denoted Intelligent Realities (IRs). Rather than …

This paper argues for the use of a topology learning algorithm, the Growing Neural Gas
(GNG), for providing an overview of the structure of large and multidimensional datasets that …

Machine learning is becoming increasingly important to control the behavior of safety and
financially critical components in sophisticated environments, where the inability to …

Abstract Enterprise Resource Planning (ERP) software is used by businesses and extended
via customisation. Automated custom code analysis and migration is a critical issue at ERP …

As Artificial Intelligence spreads into more and more areas of our lives, not least our work
lives, political and regulatory aspects are in need of revision. We analyzed the current …