Cecil, Julia and Lermer, Eva and Hudecek, Matthias F. C. and Sauer, Jan and Gaube, Susanne (2024) Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task. SCIENTIFIC REPORTS, 14 (1): 9736. ISSN 2045-2322
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Despite the rise of decision support systems enabled by artificial intelligence (AI) in personnel selection, their impact on decision-making processes is largely unknown. Consequently, we conducted five experiments (N = 1403 students and Human Resource Management (HRM) employees) investigating how people interact with AI-generated advice in a personnel selection task. In all pre-registered experiments, we presented correct and incorrect advice. In Experiments 1a and 1b, we manipulated the source of the advice (human vs. AI). In Experiments 2a, 2b, and 2c, we further manipulated the type of explainability of AI advice (2a and 2b: heatmaps and 2c: charts). We hypothesized that accurate and explainable advice improves decision-making. The independent variables were regressed on task performance, perceived advice quality and confidence ratings. The results consistently showed that incorrect advice negatively impacted performance, as people failed to dismiss it (i.e., overreliance). Additionally, we found that the effects of source and explainability of advice on the dependent variables were limited. The lack of reduction in participants' overreliance on inaccurate advice when the systems' predictions were made more explainable highlights the complexity of human-AI interaction and the need for regulation and quality standards in HRM.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | BIAS; Artificial intelligence; Personnel selection; Decision-making |
| Subjects: | 100 Philosophy & psychology > 150 Psychology |
| Divisions: | Human Sciences > Institut für Psychologie > Lehrstuhl für Psychologie V (Sozial-, Arbeits- und Organisationspsychologie) - Prof. Dr. Peter Fischer |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 15 Jan 2026 07:26 |
| Last Modified: | 15 Jan 2026 07:26 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64878 |
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