Coussement, Kristof and Abedin, Mohammad Zoynul and Kraus, Mathias and Maldonado, Sebastian and Topuz, Kazim (2024) Explainable AI for enhanced decision-making. DECISION SUPPORT SYSTEMS, 184: 114276. ISSN 0167-9236, 1873-5797
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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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Explainable artificial intelligence; Interpretability; Visualizations |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Information Systems > Chair of Explainable Artificial Inteligence for Business Value Creation (Prof. Dr. Mathias Kraus) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 26 Nov 2025 09:52 |
| Last Modified: | 26 Nov 2025 09:52 |
| URI: | https://pred.uni-regensburg.de/id/eprint/65191 |
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