The focus of this project is on crucial overarching properties of decisions and explanations. The aim is to investigate the important question of how a person’s critical attitude towards an AI system can be supported by fostering a healthy distrust in intelligent systems, and whether and how this attitude can be reinforced by means of explainable machine learning methods. This attitude is crucial for persons to act mindfully and be empowered to shape AI. An attitude of healthy distrust towards intelligent systems and their outputs is needed in order to prevent either disuse (e.g. a complete unwillingness to use the system) or overtrusting (e.g. blind trust) of an intelligent system. By fostering an attitude of healthy distrust a person should be able to appropriately rely on intelligent systems in potentially high-risk domains such as medicine. This project benefits from interdisciplinary expertise in experimental studies and formal modeling as well as from being able to use cases from the domain of machine learning. By combining expertise from the fields of Machine Learning and Psychology the researchers will be able to investigate the psychological underpinnings of user trust and how healthy distrust can be fostered by and in the interaction with intelligent systems, ML, and XAI.