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