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Machine learning applications have become ubiquitous. Their applicationsrange from embedded control in production machines over process optimization indiverse areas (e.g., traffic, finance, sciences) to direct user interactionslike advertising and recommendations. This has led to an increased effort ofmaking machine learning trustworthy. Explainable and fair AI have alreadymatured. They address the knowledgeable user and the application engineer.However, there are users that want to deploy a learned model in a similar wayas their washing machine. These stakeholders do not want to spend time inunderstanding the model, but want to rely on guaranteed properties. What arethe relevant properties? How can they be expressed to the stakeholder withoutpresupposing machine learning knowledge? How can they be guaranteed for acertain implementation of a machine learning model? These questions move farbeyond the current state of the art and we want to address them here. Wepropose a unified framework that certifies learning methods via care labels.They are easy to understand and draw inspiration from well-known certificateslike textile labels or property cards of electronic devices. Our frameworkconsiders both, the machine learning theory and a given implementation. We testthe implementation's compliance with theoretical properties and bounds.

Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework