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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jana Gerlach & Paul Hoppe & Sarah Jagels & Luisa Licker & Michael H. Breitner, 2022. "Decision support for efficient XAI services - A morphological analysis, business model archetypes, and a decision tree," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2139-2158, December.
    • Handle: RePEc:spr:elmark:v:32:y:2022:i:4:d:10.1007_s12525-022-00603-6
    DOI: 10.1007/s12525-022-00603-6
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    References listed on IDEAS

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    Cited by:

    1. Tachia Chin & Muhammad Waleed Ayub Ghouri & Jiyang Jin & Muhammet Deveci, 2024. "AI technologies affording the orchestration of ecosystem-based business models: the moderating role of AI knowledge spillover," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    2. Christian Meske & Babak Abedin & Mathias Klier & Fethi Rabhi, 2022. "Explainable and responsible artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2103-2106, December.
    3. Jorzik, Philip & Klein, Sascha P. & Kanbach, Dominik K. & Kraus, Sascha, 2024. "AI-driven business model innovation: A systematic review and research agenda," Journal of Business Research, Elsevier, vol. 182(C).

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    More about this item

    Keywords

    Artificial intelligence; Explainability; Morphological analysis; Business models; Archetypes; Decision tree;
    All these keywords.

    JEL classification:

    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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