Designing Human-Automation Redundancy: Overreliance, Automation Shirking, and the Operator’s Dilemma
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Even when agents are nominally aligned, optimization for latency or engagement can favor low-friction interfaces that increase the likelihood users accept unsafe defaults, skip verification, and disclose sensitive information.
“Even when agents are nominally aligned, optimization for latency or engagement can favor low-friction interfaces that increase the likelihood users accept unsafe defaults, skip verification, and disclose sensitive information (Roth et al., 2024); (Cohen et al., 2025).”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Cohen et al., 2025
Cites
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · see citing works
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755 · see citing works
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 · see citing works
Automation bias: a systematic review of frequency, effect mediators, and mitigators
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000089 · Cited by 1,162 · see citing works
Does automation bias decision-making?
SKITKA, L. J., MOSIER, K. L., & BURDICK, M. (1999) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.1999.0252 · Cited by 694 · see citing works
When combinations of humans and AI are useful: A systematic review and meta-analysis
Vaccaro, M., Almaatouq, A., & Malone, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02024-1 · Cited by 570 · see citing works
Do as AI say: susceptibility in deployment of clinical decision-aids
Gaube, S., Suresh, H., Raue, M., Merritt, A., Berkowitz, S. J., Lermer, E., Coughlin, J. F., Guttag, J. V., Colak, E., & Ghassemi, M. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00385-9 · Cited by 543 · see citing works
Automation bias and verification complexity: a systematic review
Lyell, D., & Coiera, E. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw105 · Cited by 451 · see citing works
Human-machine collaboration in managerial decision making
Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106730 · Cited by 280 · see citing works
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
Liao, Q. V., & Vaughan, J. W. (2023) · arXiv · doi:10.48550/arxiv.2306.01941 · Cited by 209 · see citing works
Large Language Model in Creative Work: The Role of Collaboration Modality and User Expertise
Chen, Z., & Chan, J. (2024) · Management Science · doi:10.1287/mnsc.2023.03014 · Cited by 204 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
de Witt, C. S., Krawiecka, K., Krawczuk, I., Hagag, B., Anderson, W. L., Belcak, P., Bucknall, B., Cai, X., Chopra, A., Cohen, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.02077 · Cited by 2 · see citing works
Self-citation
- Authors
- Cohen, D., Roth, Y., Rieskamp, J., & Schöbel, M.
- Affiliations
- Carnegie Mellon University (US)
- University of Haifa (IL)
- University of Basel (CH)
- Corresponding author
- Cohen, Doron
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2025
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4407831729
- Cited by
- 0
- References
- 11
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Risk and Safety Analysis
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- over-reliance on automation, operator behavior, safety inspection, automation transparency, crash risk, semi-autonomous systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
