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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.

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How Explainable AI Methods Support Data-Driven Decision-Making

  • 会议论文

  • 发表日期：

    2025年

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  • Dominik Stoffels,
  • Susanne Grabl,
  • Thomas Fischer,
  • Marina Fiedler

摘要

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.