With the availability of large databases and recent improvements in deep learning
methodology, the performance of AI systems is reaching or even exceeding the human level …

In recent years, machine learning (ML) has become a key enabling technology for the
sciences and industry. Especially through improvements in methodology, the availability of …

Issues regarding explainable AI involve four components: users, laws and regulations,
explanations and algorithms. Together these components provide a context in which …

The development of “intelligent” systems that can take decisions and perform autonomously
might lead to faster and more consistent decisions. A limiting factor for a broader adoption of …

Interpretability is a fundamental property for the acceptance of machine learning models in
highly regulated areas. Recently, deep neural networks gained the attention of the scientific …

Recently, artificial intelligence and machine learning in general have demonstrated
remarkable performances in many tasks, from image processing to natural language …

Recent advancements in machine learning and signal processing domains have resulted in
an extensive surge of interest in Deep Neural Networks (DNNs) due to their unprecedented …

Recently, deep learning has been advancing the state of the art in artificial intelligence to a
new level, and humans rely on artificial intelligence techniques more than ever. However …

Despite their success, deep networks are used as black-box models with outputs that are not
easily explainable during the learning and the prediction phases. This lack of interpretability …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …