  The diffusion of artificial intelligence (AI) applications in organizations
and society has fueled research on explaining AI decisions. The explainable AI
(xAI) field is rapidly expanding with numerous ways of extracting information
and visualizing the output of AI technologies (e.g. deep neural networks). Yet,
we have a limited understanding of how xAI research addresses the need for
explainable AI. We conduct a systematic review of xAI literature on the topic
and identify four thematic debates central to how xAI addresses the black-box
problem. Based on this critical analysis of the xAI scholarship we synthesize
the findings into a future research agenda to further the xAI body of
knowledge.
