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Towards the Design of an Interactive Machine Learning System for Qualitative Coding

Rietz, Tim; Maedche, Alexander

Coding is an important process in qualitative research. However, qualitative coding is highly time-consuming even for small datasets. To accelerate this process, qualitative coding systems increasingly utilize machine learning (ML) to automatically recommend codes. Existing literature on ML-assisted coding reveals two major issues: (1) ML model training is not well integrated into the qualitative coding process, and (2) code recommendations need to be presented in a trustworthy way. We believe that the recently introduced concept of interactive machine learning (IML) is able to address these issues. We present an ongoing design science research project to design an IML system for qualitative coding. First, we discover several issues that hinder the success of current ML-based coding systems. Drawing on results from multiple fields, we derive meta-requirements, propose design principles and an initial prototype. Thereby, we contribute with design knowledge for the intelligent augmentation of qualitative coding systems to increase coding productivity.

Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 12.2020
Sprache Englisch
Identifikator KITopen-ID: 1000124563
Erschienen in ICIS 2020 – Making Digital Inclusive: Blending the Local and the Global, December 13-16, 2020
Veranstaltung International Conference on Information Systems (ICIS 2020), Online, 13.12.2020 – 16.12.2020
Bemerkung zur Veröffentlichung Die Veranstaltung fand wegen der Corona-Pandemie als Online-Event statt
Vorab online veröffentlicht am 13.10.2020
Schlagwörter Qualitative Coding, Interactive Machine Learning, Design Science Research, Design Principles
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