How Explainable AI Methods Support Data-Driven Decision-Making

Dominik Stoffels (), Susanne Grabl (), Thomas Fischer () and Marina Fiedler ()
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Dominik Stoffels: University of Passau
Susanne Grabl: University of Passau
Thomas Fischer: University of Passau
Marina Fiedler: University of Passau

A chapter in Conceptualizing Digital Responsibility for the Information Age, 2025, pp 325-340 from Springer

Abstract: 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.

Keywords: Explainable AI; Machine learning; Data-driven decision-making (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-80119-8_21

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DOI: 10.1007/978-3-031-80119-8_21

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