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- ZeitschriftenartikelPower to the Oracle? Design Principles for Interactive Labeling Systems in Machine Learning(KI - Künstliche Intelligenz: Vol. 34, No. 2, 2020) Nadj, Mario; Knaeble, Merlin; Li, Maximilian Xiling; Maedche, AlexanderLabeling is the process of enclosing information to some object. In machine learning it is required as ground truth to leverage the potential of supervised techniques. A key challenge in labeling is that users are not necessarily eager to behave as simple oracles, that is, repeatedly answering questions whether a label is right or wrong. In this respect, scholars acknowledge designing interactivity in labeling systems as a promising area for further improvements. In recent years, a considerable number of articles focusing on interactive labeling systems have been published. However, there is a lack of consolidated principles how to design these systems. In this article, we identify and discuss five design principles for interactive labeling systems based on a literature review and offer a frame for detecting common ground in the implementation of corresponding solutions. With these guidelines, we strive to contribute design knowledge for the increasingly important class of interactive labeling systems.