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.

中文翻译：

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基于人工智能的系统如何引发反思：人工智能增强诊断工作案例

本文讨论了人类人工智能 (AI) 增强中迄今为止被忽视的一个维度：机器引起的反射。通过建立机器诱导反射的基础理论模型，我们为信息系统（IS）中关于人工智能和反射理论研究的持续讨论做出了贡献。在我们的多阶段研究中，医生使用基于机器学习 (ML) 的临床决策支持系统 (CDSS) 来了解这种交互是否以及如何在 X 射线诊断任务中激发反思性实践。通过分析口头协议、绩效指标和调查数据，我们开发了一个综合的理论基础来解释基于机器学习的系统如何帮助激发反思实践。个人会根据他们是否认为与这些 CDSS 系统存在冲突或同意而采取更批判或更浅薄的模式，这反过来又会导致不同程度的反思深度。通过揭示机器引发的反思过程，我们为信息系统研究提供了一个不同的视角，即此类基于人工智能的系统如何帮助个人变得更具反思性，从而变得更高效的专业人士。这种观点与基于机器学习的决策支持系统注重效率的传统观点形成鲜明对比，也丰富了人类人工智能增强的理论。