@inproceedings{bea750e58d0743bbaabe657b95a321aa,
title = "How Explainable AI Methods Support Data-Driven Decision-Making",
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 = "Data-driven decision-making, Explainable AI, Machine learning",
author = "Dominik Stoffels and Susanne Grabl and Thomas Fischer and Marina Fiedler",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 18th International Conference on Wirtschaftsinformatik, WI 2023 ; Conference date: 18-09-2023 Through 21-09-2023",
year = "2025",
doi = "10.1007/978-3-031-80119-8\_21",
language = "English",
isbn = "9783031801181",
series = "Lecture Notes in Information Systems and Organisation",
publisher = "Springer",
pages = "325--340",
editor = "Daniel Beverungen and Matthias Trier and Christiane Lehrer",
booktitle = "Conceptualizing Digital Responsibility for the Information Age - Proceedings of the 18th International Conference on Wirtschaftsinformatik, 2023, Vol. 1",
address = "Germany",
}