CC BY 4.0Becker, MaximilianMaximilianBecker2023-07-192023-07-192023https://publica.fraunhofer.de/handle/publica/445799https://doi.org/10.24406/publica-165510.24406/publica-1655Machine learning systems are often hard to investigate and intransparent in their decision making . Explainable Artificial Intelligence (XAI) tries to make these systems more transparent. However, most work in the field focuses on technical aspects like maximizing metrics. The human aspects of explainability are often neglected. In this work, we present personalized explanations, which instead focus on the user. Personalized explanations can be adapted to individual users to be as useful and relevant as possible. They can be interacted with to give users the ability to engage in an explanatory dialog with the system. Finally, they should also protect user data to increase the trust in the explanation system.enconference paper