Interpretable machine learning (IML) is concerned with the behavior and the properties of machine learning models. Scientists, however, are only interested in models as a gateway to understanding phenomena. Our work aligns these two perspectives and shows how to design IML property descriptors. These descriptors are IML methods that provide insight not just into the model, but also into the properties of the phenomenon the model is designed to represent. We argue that IML is necessary for scientific inference with ML models because their elements do not individually represent phenomenon properties; instead, the model in its entirety does. However, current IML research often conflates two goals of model analysis -- model audit and scientific inference -- making it unclear which model interpretations can be used to learn about phenomena. Building on statistical decision theory, we show that IML property descriptors applied on a model provide access to relevant aspects of the joint probability distribution of the data. We identify what questions such descriptors can address, provide a guide to building appropriate descriptors and quantify their epistemic uncertainty.