Explainable AI in medical technology and automotive applications

When it comes to the subject of machine learning and deep neural networks (DNNs), there are usually more questions than answers, as data analysis by ML-based systems remains an enigmatic process for developers and users alike. Nevertheless, it is vital that the systems provide transparency and interpretability – particularly with regard to safety issues in the automotive sector, such as driver drowsiness detection, or in medicine, with automated screening of tissue samples.

The use of automatic processes in critical strategic decision-making is dependent on the explainability of data analysis, which also underpins the general acceptance of these processes.

The aim of the research focus »Explainable AI for medicine and the automotive sector« is to create new methods of explainable machine learning in combination with prediction and prescription (explanation) in the stated areas of application. This includes methods intended not only to ensure transparency in the training and working methods of DNNs, but also to improve the validatability of forecast model content.