With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

For a machine learning model to generalize well, one needs to ensure that its decisions are
supported by meaningful patterns in the input data. A prerequisite is however for the model …

The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …

Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

Abstract Explainable Artificial Intelligence (xAI) is an established field with a vibrant
community that has developed a variety of very successful approaches to explain and …

Current learning machines have successfully solved hard application problems, reaching
high accuracy and displaying seemingly intelligent behavior. Here we apply recent …

In addition to the impressive predictive power of machine learning (ML) models, more
recently, explanation methods have emerged that enable an interpretation of complex …

Abstract Machine learning (ML) techniques such as (deep) artificial neural networks (DNN)
are solving very successfully a plethora of tasks and provide new predictive models for …

We ask whether the neural network interpretation methods can be fooled via adversarial
model manipulation, which is defined as a model fine-tuning step that aims to radically alter …

Within the last decade, neural network based predictors have demonstrated impressive-and
at times superhuman-capabilities. This performance is often paid for with an intransparent …