  Machine learning applications have become ubiquitous. Their applications
range from embedded control in production machines over process optimization in
diverse areas (e.g., traffic, finance, sciences) to direct user interactions
like advertising and recommendations. This has led to an increased effort of
making machine learning trustworthy. Explainable and fair AI have already
matured. They address the knowledgeable user and the application engineer.
However, there are users that want to deploy a learned model in a similar way
as their washing machine. These stakeholders do not want to spend time in
understanding the model, but want to rely on guaranteed properties. What are
the relevant properties? How can they be expressed to the stakeholder without
presupposing machine learning knowledge? How can they be guaranteed for a
certain implementation of a machine learning model? These questions move far
beyond the current state of the art and we want to address them here. We
propose a unified framework that certifies learning methods via care labels.
They are easy to understand and draw inspiration from well-known certificates
like textile labels or property cards of electronic devices. Our framework
considers both, the machine learning theory and a given implementation. We test
the implementation's compliance with theoretical properties and bounds.
