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How Explainable AI Methods Support Data-Driven Decision-Making

Author

Listed:
  • Dominik Stoffels

    (University of Passau)

  • Susanne Grabl

    (University of Passau)

  • Thomas Fischer

    (University of Passau
    University of Applied Sciences Upper Austria)

  • Marina Fiedler

    (University of Passau)

Abstract

Explainable AI (XAI) holds great potential to reveal the patterns in black-box AI models and to support data-driven decision-making. We apply four post-hoc explanatory methods to demonstrate the explanatory capabilities of these methods for data-driven decision-making using the illustrative example of unwanted job turnover and human resource management (HRM) support. We show that XAI can be a useful aid in data-driven decision-making, but also highlight potential drawbacks and limitations of which users in research and practice should be aware.

Suggested Citation

  • Dominik Stoffels & Susanne Grabl & Thomas Fischer & Marina Fiedler, 2025. "How Explainable AI Methods Support Data-Driven Decision-Making," Lecture Notes in Information Systems and Organization,, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-80119-8_21
    DOI: 10.1007/978-3-031-80119-8_21
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