Decision support for efficient XAI services - A morphological analysis, business model archetypes, and a decision tree

Jana Gerlach (), Paul Hoppe, Sarah Jagels, Luisa Licker and Michael H. Breitner
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Jana Gerlach: Leibniz Universität Hannover
Paul Hoppe: Leibniz Universität Hannover
Sarah Jagels: Leibniz Universität Hannover
Luisa Licker: Leibniz Universität Hannover
Michael H. Breitner: Leibniz Universität Hannover

Electronic Markets, 2022, vol. 32, issue 4, No 18, 2139-2158

Abstract: Abstract 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.

Keywords: Artificial intelligence; Explainability; Morphological analysis; Business models; Archetypes; Decision tree (search for similar items in EconPapers)
JEL-codes: M15 M21 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s12525-022-00603-6

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