The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
The Effect of Workload and Task Priority on Multitasking Performance and Reliance on Level 1 Explainable AI (XAI) Use
Abstract
Objective: This study investigates the effects of workload and task priority on multitasking performance and reliance on Level 1 Explainable Artificial Intelligence (XAI) systems in high-stakes decision environments. Background: Operators in critical settings manage multiple tasks under varying levels of workload and priority, potentially leading to performance degradation. XAI offers opportunities to support decision making by providing insights into AI’s reasoning, yet its adoption and effectiveness in multitasking scenarios remain underexplored. Method: Thirty participants engaged in a simulated multitasking environment, involving UAV command and control tasks, with the assistance of a Level 1 (i.e., basic perceptual information) XAI system on one of the tasks. The study utilized a within-subjects experimental design, manipulating workload (low, medium, and high) and AI-supported-task priority (low and high) across six conditions. Participants’ accuracy, use of automatic rerouting, AI miss detection, false alert identification, and use of AI explanations were measured and analyzed across the different experimental conditions. Results: Workload significantly hindered performance on the AI-assisted task and increased reliance on the AI system especially when the AI-assisted task was given low priority. The use of AI explanations was significantly affected by task priority only. Conclusion: An increase in workload led to proper offloading by relying on the AI’s alerts, but it also led to a lower rate of alert verification despite the alert feature’s high false alert rate. Application: The findings from the present work help inform AI system designers on how to design their systems for high-stakes environments such that reliance on AI is properly calibrated.
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Citations
Cites
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384
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Advances in Neural Information Processing Systems 29
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Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 1×
“As trust is often de fi ned as the belief that an agent will help achieve an individual ’ s goals in situations characterized by uncertainty and vulnerability (Lee & See, 2004), this over-reliance could have been due to over-trust.”
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817
Complacency and Bias in Human Use of Automation: An Attentional Integration
Parasuraman, R., & Manzey, D. H. (2010) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720810376055 · Cited by 1,710 · cited 1×
“…and limitations that can result in under-reliance , where operators choose to exclude the AI from their work fl ow, or overreliance , where operators blindly rely on its decisions despite its susceptibility to errors (Buçinca et al., 2021; Parasuraman & Manzey, 2010; Vasconcelos et al., 2023).”
DARPA's Explainable Artificial Intelligence Program
Gunning, D., & Aha, D. W. (2019) · AI Magazine · doi:10.1609/aimag.v40i2.2850 · Cited by 1,360
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021) · arXiv · doi:10.48550/arxiv.2102.09692 · Cited by 1,124 · cited 1×
“…and limitations that can result in under-reliance , where operators choose to exclude the AI from their work fl ow, or overreliance , where operators blindly rely on its decisions despite its susceptibility to errors (Buçinca et al., 2021; Parasuraman & Manzey, 2010; Vasconcelos et al., 2023).”
Research works printed but not linked: 3
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- Shattuck L. G. (2015). Transitioning to autonomy: A human systems integration perspective. In Transitioning to autonomy: Changes in the role of humans in air transportation (pp. 10–12). NACC.
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Cited by
Exploring automation bias in human–AI collaboration: a review and implications for explainable AI
Romeo, G., & Conti, D. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02422-7 · Cited by 209
“Occurrences of overreliance may also be understood as strategies to reduce cognitive load, rather than as mere manifestations of misplaced trust (Alami et al. 2025).”
Influential
Metadata
- Authors
- Alami, J., El Iskandarani, M., & Riggs, S. L.
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2025-03-12
- Volume
- 67
- Issue
- 9
- Pages
- 897-915
- ISSN
- 0018-7208
- 1547-8181
- Cited by
- 9
- References
- 45
- Language
- en
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
- Funders
- Division of Information and Intelligent Systems (2008680)
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