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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.

DOI: 10.1007/s13218-020-00634-1
Zitationen: 6
Zugehörige Institution(en) am KIT Fakultät für Wirtschaftswissenschaften – 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
Verlag Springer
Band 34
Seiten 131–142
Vorab online veröffentlicht am 11.01.2020
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