Power to the Oracle? Design Principles for Interactive Labeling Systems in Machine Learning
| dc.contributor.author | Nadj, Mario | |
| dc.contributor.author | Knaeble, Merlin | |
| dc.contributor.author | Li, Maximilian Xiling | |
| dc.contributor.author | Maedche, Alexander | |
| dc.date.accessioned | 2021-04-23T09:34:07Z | |
| dc.date.available | 2021-04-23T09:34:07Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | 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. | de |
| dc.identifier.doi | 10.1007/s13218-020-00634-1 | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s13218-020-00634-1 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36288 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 34, No. 2 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Interactive labeling | |
| dc.subject | Interactive machine learning | |
| dc.subject | Training data | |
| dc.title | Power to the Oracle? Design Principles for Interactive Labeling Systems in Machine Learning | de |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 142 | |
| gi.citation.startPage | 131 |