HOW AI-BASED SYSTEMS CAN INDUCE REFLECTIONS: THE CASE OF AI-AUGMENTED DIAGNOSTIC WORK.
In: MIS Quarterly, Jg. 47 (2023-12-01), Heft 4, S. 1395-1423
academicJournal
Zugriff:
This paper addresses a thus-far neglected dimension in human-artificial intelligence (AI) augmentation: machine-induced reflections. By establishing a grounded theoretical-informed model of machine-induced reflection, we contribute to the ongoing discussion in information systems (IS) regarding AI and research on reflection theories. In our multistage study, physicians used a machine learning-based (ML) clinical decision support system (CDSS) to see if and how this interaction can stimulate reflective practice in the context of an X-ray diagnosis task. By analyzing verbal protocols, performance metrics, and survey data, we developed an integrative theoretical foundation to explain how ML-based systems can help stimulate reflective practice. Individuals engage in more critical or shallower modes depending on whether they perceive a conflict or agreement with these CDSS systems, which in turn leads to different levels of reflection depth. By uncovering the process of machine-induced reflections, we offer IS research a different perspective on how such AI-based systems can help individuals become more reflective, and consequently more effective, professionals. This perspective stands in stark contrast to the traditional, efficiency-focused view of MLbased decision support systems and also enriches theories on human-AI augmentation. [ABSTRACT FROM AUTHOR]
Titel: |
HOW AI-BASED SYSTEMS CAN INDUCE REFLECTIONS: THE CASE OF AI-AUGMENTED DIAGNOSTIC WORK.
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Autor/in / Beteiligte Person: | Abdel-Karim, Benjamin M. ; Pfeuffer, Nicolas ; Carl, K. Valerie ; Hinz, Oliver |
Zeitschrift: | MIS Quarterly, Jg. 47 (2023-12-01), Heft 4, S. 1395-1423 |
Veröffentlichung: | 2023 |
Medientyp: | academicJournal |
ISSN: | 0276-7783 (print) |
DOI: | 10.25300/MISQ/2022/16773 |
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