Machine Learning (ML) is increasingly being adopted in Information Systems (IS) research and practice, accompanied by growing fears of not being able to understand ML-based systems and by demands for controllability and transparency. This study presents a scoping review of the emergent area of Explainable and Interactive Machine Learning (XIML) that may answer the societal demands by combining approaches to explainable Artificial Intelligence and Interactive Machine Learning. In searching relevant databases with appropriate keywords within the scope of XIML, 33 articles are found to deal with XIML research so far. In further analyzing what these articles were able to contribute, four interesting avenues of research for IS scholars were identified that promise fruitful opportunities based on relevance, impact and theory for combining IS and XIML research.
Pfeuffer, Nicolas, "Explainability in Interactive Machine Learning: Novel Avenues for Information Systems Research" (2021). PACIS 2021 Proceedings. 231.
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