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Cody: An AI-Based System to Semi-Automate Coding for Qualitative Research

Rietz, Tim; Maedche, Alexander


Qualitative research can produce a rich understanding of a phenomenon but requires an essential and strenuous data annotation process known as coding. Coding can be repetitive and time-consuming, particularly for large datasets. Existing AI-based approaches for partially automating coding, like supervised machine learning (ML) or explicit knowledge represented in code rules, require high technical literacy and lack transparency. Further, little is known about the interaction of researchers with AI-based coding assistance. We introduce Cody, an AI-based system that semi-automates coding through code rules and supervised ML. Cody supports researchers with interactively (re)defining code rules and uses ML to extend coding to unseen data. In two studies with qualitative researchers, we found that (1) code rules provide structure and transparency, (2) explanations are commonly desired but rarely used, (3) suggestions benefit coding quality rather than coding speed, increasing the intercoder reliability, calculated with Krippendorff’s Alpha, from 0.085 (MAXQDA) to 0.33 (Cody).

Postprint §
DOI: 10.5445/IR/1000128503
Veröffentlicht am 09.05.2022
DOI: 10.1145/3411764.3445591
Zitationen: 15
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 08.05.2021
Sprache Englisch
Identifikator ISBN: 978-1-4503-8096-6
KITopen-ID: 1000128503
Erschienen in Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2021) : Yokohama, Japan, Ed.: Y. Kitamura
Veranstaltung Conference on Human Factors in Computing Systems (CHI 2021), Online, 08.05.2021 – 13.05.2021
Verlag Association for Computing Machinery (ACM)
Seiten Art.Nr. 394
Schlagwörter Qualitative research; Qualitative coding; Rule-based coding; Supervised machine learning; User-centered design; Artifact design
Nachgewiesen in Dimensions
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