Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/311920 
Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] Electronic Markets [ISSN:] 1422-8890 [Volume:] 32 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 2139-2158
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
The black-box nature of Artificial Intelligence (AI) models and their associated explainability limitations create a major adoption barrier. Explainable Artificial Intelligence (XAI) aims to make AI models more transparent to address this challenge. Researchers and practitioners apply XAI services to explore relationships in data, improve AI methods, justify AI decisions, and control AI technologies with the goals to improve knowledge about AI and address user needs. The market volume of XAI services has grown significantly. As a result, trustworthiness, reliability, transferability, fairness, and accessibility are required capabilities of XAI for a range of relevant stakeholders, including managers, regulators, users of XAI models, developers, and consumers. We contribute to theory and practice by deducing XAI archetypes and developing a user-centric decision support framework to identify the XAI services most suitable for the requirements of relevant stakeholders. Our decision tree is founded on a literature-based morphological box and a classification of real-world XAI services. Finally, we discussed archetypical business models of XAI services and exemplary use cases.
Schlagwörter: 
Artificial intelligence
Explainability
Morphological analysis
Business models
Archetypes
Decision tree
JEL: 
M150
M210
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Dokumentart: 
Article
Dokumentversion: 
Published Version

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