How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work

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Publication History

Received: March 21, 2020
Revised: March 6, 2021; December 23, 2021; July 19, 2022; December 6, 2022
Accepted: December 9, 2022
Published Online as Articles in Advance: November 30, 2023
Published Online in Issue: December 1, 2023

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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 ML-based decision support systems and also enriches theories on human-AI augmentation.

Additional Details
Author Benjamin M. Abdel-Karim, Nicolas Pfeuffer, K. Valerie Carl, and Oliver Hinz
Year 2023
Volume 47
Issue 4
Keywords Machine Learning, reflective practice, grounded theory, health information technology, physicians, verbal protocols
Page Numbers 1395-1424
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