Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …

We have witnessed the impact of ML in disease diagnosis, image recognition and
classification, and many more related fields. Healthcare is a sensitive field related to …

Society increasingly relies on machine learning models for automated decision making. Yet,
efficiency gains from automation have come paired with concern for algorithmic …

Artificial Intelligence (AI) plays an increasingly integral role in decision-making processes. In
order to foster trust in AI predictions, many approaches towards explainable AI (XAI) have …

Interpretable Machine Learning (IML) has become increasingly important in many real-world
applications, such as autonomous cars and medical diagnosis, where explanations are …

Active learning methods increase classification quality by means of user feedback. An
important subcategory is active learning for outlier detection with one-class classifiers. While …

While halide perovskites attract significant academic attention, examples of industrial
production at scale are still sparse. In this perspective, we review practical challenges …

Explaining the behavior of a black box machine learning model at the instance level is
useful for building trust. However, it is also important to understand how the model behaves …

The impact of Artificial Intelligence (AI) on health care has been dramatic; however, there is
a considerable degree of skepticism among clinicians about the real-world applicability of …