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Power to the Oracle? Design Principles for Interactive Labeling Systems in Machine Learning

Nadj, Mario; Knaeble, Merlin; Li, Maximilian Xiling; Maedche, Alexander

Abstract (englisch):
Labeling 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.



Originalveröffentlichung
DOI: 10.1007/s13218-020-00634-1
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 0933-1875, 1610-1987
KITopen-ID: 1000104859
Erschienen in Künstliche Intelligenz
Seiten 1–12
Vorab online veröffentlicht am 11.01.2020
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