Artificial intelligence (AI) has ubiquitous applications in companies, permeating multiple business divisions like human resource management (HRM). Yet, in these high-stakes domains where transparency and interpretability of results are of utmost importance, the black-box characteristic of AI is even more of a threat to AI adoption. Hence, explainable AI (XAI), which is regular AI equipped with or complemented by techniques to explain it, comes in. We present a systematic literature review of n=62 XAI in HRM papers. Further, we conducted an experiment among a German sample (n=108) of HRM personnel regarding a turnover prediction task with or without (X)AI-support. We find that AI-support leads to better task performance, self-assessment accuracy and response characteristics toward the AI, and XAI, i.e., transparent models allow for more accurate self-assessment of one’s performance. Future studies could enhance our research by employing local explanation techniques on real-world data with a larger and international sample.
Baum, Lorenz; Weber, Patrick; and Kolb, Laura-Marie, "The Explanation Matters: Enhancing AI Adoption in Human Resource Management" (2023). PACIS 2023 Proceedings. 17.
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