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Machine learning applications have become ubiquitous. This has led to anincreased effort of making machine learning trustworthy. Explainable and fairAI have already matured. They address knowledgeable users and applicationengineers. For those who do not want to invest time into understanding themethod or the learned model, we offer care labels: easy to understand at aglance, allowing for method or model comparisons, and, at the same time,scientifically well-based. On one hand, this transforms descriptions as givenby, e.g., Fact Sheets or Model Cards, into a form that is well-suited forend-users. On the other hand, care labels are the result of a certificationsuite that tests whether stated guarantees hold. In this paper, we present twoexperiments with our certification suite. One shows the care labels forconfigurations of Markov random fields (MRFs). Based on the underlying theoryof MRFs, each choice leads to its specific rating of static properties like,e.g., expressivity and reliability. In addition, the implementation is testedand resource consumption is measured yielding dynamic properties. Thistwo-level procedure is followed by another experiment certifying deep neuralnetwork (DNN) models. There, we draw the static properties from the literatureon a particular model and data set. At the second level, experiments aregenerated that deliver measurements of robustness against certain attacks. Weillustrate this by ResNet-18 and MobileNetV3 applied to ImageNet.

The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning