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Towards an Integrative Theoretical Framework of Interactive Machine Learning Systems

Meza Martínez, Miguel Angel ORCID iD icon; Nadj, Mario 1; Maedche, Alexander 1
1 Karlsruher Institut für Technologie (KIT)


Interactive machine learning (IML) is a learning process in which a user interacts with a system to iteratively define and optimise a model. Although recent years have illustrated the proliferation of IML systems in the fields of Human-Computer Interaction (HCI), Information Systems (IS), and Computer Science (CS), current research results are scattered leading to a lack of integration of existing work on IML. Furthermore, due to diverging functionalities and purposes IML systems can refer to, an uncertainty exists regarding the underlying distinct capabilities that constitute this class of systems. By reviewing extensive IML literature, this paper suggests an integrative theoretical framework for IML systems to address these current impediments. Reviewing 2,879 studies in leading journals and conferences during the years 1966-2018, we found an extensive range of applications areas that have implemented IML systems and the necessity to standardise the evaluation of those systems. Our framework offers an essential step to provide a theoretical foundation to integrate concepts and findings across different fields of research. The main contribution of this paper is organising and structuring the body of knowledge in IML for the advancement of the field. ... mehr

Zitationen: 4
Zugehörige Institution(en) am KIT Fakultät für Wirtschaftswissenschaften – Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2019
Sprache Englisch
Identifikator ISBN: 978-1-73363-250-8
KITopen-ID: 1000095235
Erschienen in ECIS 2019 proceedings . 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden, June 8-14, 2019. Research Papers
Veranstaltung 27th European Conference on Information Systems (ECIS 2019), Stockholm/Uppsala, 08.06.2019 – 14.06.2019
Verlag AIS eLibrary (AISeL)
Seiten Paper: 172
Externe Relationen Abstract/Volltext
Schlagwörter information systems; interaction; interactive machine learning; systematic literature review
Nachgewiesen in Scopus
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