Explainable AI for enhanced decision-making

Abstract

This paper contextualizes explainable artificial intelligence (AI) for enhanced decision-making and serves as an editorial for the corresponding special issue. AI is defined as the development of computer systems that are able to perform tasks that normally require human intelligence by understanding, processing, and analyzing large amounts of data. AI has been a dominant domain for several decades in the information systems (IS) literature. To this end, we define explainable AI (XAI) as the process that allows one to understand how an AI system decides, predicts, and performs its operations. First, we contextualize its current role for improved business decision-making. Second, we discuss three underlying dimensions of XAI that serve as broader innovation grounds to make better and more informed decisions, i.e., data, method, and application. For each of the contributing papers in this special issue, we describe their major contributions to the field of XAI for decision making. In conclusion, this paper further presents a future research agenda for IS researchers in the XAI field.