In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …

Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …

We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …

Deployment of modern data-driven machine learning methods, most often realized by deep
neural networks (DNNs), in safety-critical applications such as health care, industrial plant …

Methods for safety assurance suggested by the ISO 26262 automotive functional safety
standard are not sufficient for applications based on machine learning (ML). We provide a …

Artificial Intelligence applications gradually move outside the safe walls of research labs and
invade our daily lives. This is also true for Machine Learning methods on Knowledge …

Deep Learning (DL) has gained enormous popularity recently; however, it is an opaque
technique that is regarded as a black box. To ensure the validity of the model's prediction, it …

Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially
Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces …

With the increasing number of deep learning applications, there is a growing demand for
explanations. Visual explanations provide information about which parts of an image are …

With the perspective on applications of AI-technology, especially data intensive deep
learning approaches, the need for methods to control and understand such models has …