التفاصيل البيبلوغرافية
| العنوان: |
HOW AI-BASED SYSTEMS CAN INDUCE REFLECTIONS: THE CASE OF AI-AUGMENTED DIAGNOSTIC WORK. |
| المؤلفون: |
Abdel-Karim, Benjamin M., Pfeuffer, Nicolas, Carl, K. Valerie, Hinz, Oliver |
| المصدر: |
MIS Quarterly; Dec2023, Vol. 47 Issue 4, p1395-1424, 29p |
| مستخلص: |
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] |
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| قاعدة البيانات: |
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