How Explainable AI Methods Support Data-Driven Decision Making

Authors

Dominik Stoffels, Universität Passau, GermanyFollow
Susanne Grabl, Universität Passau, GermanyFollow
Thomas Fischer, Universität Passau, GermanyFollow
Marina Fiedler, Universität Passau, GermanyFollow

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 high-light potential drawbacks and limitations of which users in research and practice should be aware.

Paper Number

211

Comments

Track 13: Advances in Theory, Methods & Philosophy

Recommended Citation

Stoffels, Dominik; Grabl, Susanne; Fischer, Thomas; and Fiedler, Marina, "How Explainable AI Methods Support Data-Driven Decision Making" (2023). Wirtschaftsinformatik 2023 Proceedings. 31.
https://aisel.aisnet.org/wi2023/31