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The benefits of imperfect diagnostic automation: a synthesis of the literature
Abstract
This review of the literature examines, in a quantitative fashion, how the level of imperfection or unreliability of diagnostic automation affects the performance of the human operator who is jointly consulting that automation and the raw data itself. The data from 20 different studies were used to generate 35 different data points that compared performance with varying levels of unreliability, with that of a non-automated baseline condition. A regression analysis of benefits/costs relative to baseline was carried out, and revealed a strong linear function of benefits with reliability. The analysis revealed that a reliability of 0.70 was the ‘crossover point’ below which unreliable automation was worse than no automation at all. The analysis also revealed that performance was more strongly affected by reliability in high workload conditions, implicating the role of workload-imposed automation dependence in producing this relationship, and suggesting that humans tend to protect performance of concurrent tasks from imperfection of diagnostic automation.
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“This observation supports prior research suggesting that both over-reliance and under-reliance are closely linked to perceived AI competence and actual system performance (Lee & See, 2004;Wickens & Dixon, 2007).”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Wickens & Dixon, 2007
In the citing paper
“Previous research has revealed consistently a positive relationship between automation reliability and the (snapshot) trust in automation measured at the end of an experiment”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Wickens & Dixon, 2007
In the citing paper
“70% is the threshold above which using automation improves task performance and below which such use may harm performance”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Wickens & Dixon, 2007
In the citing paper
“On the contrary, we find that automation reliability significantly predicts the (snapshot) trust in automation measured at the end of an experiment, which is in line with previous research (de Visser & Parasuraman, 2011; Du et al., 2020; Wickens & Dixon, 2007).”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Wickens & Dixon, 2007
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Chen, J. Y. C., Barnes, M., & Kenny, C. (2011) · doi:10.21236/ada552060 · Cited by 3
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“That is, by an improvement in some combination of the miss rate and false alarm rate of the automation diagnosis, as these measures are frequently employed to evaluate warning systems (Meyer, 2004; Meyer & Lee, 2013; Wickens & Dixon, 2007).”
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“…range from general, such as synthesizing the literature to estimate when automation performs well enough for the benefits to outweigh the costs (Wickens & Dixon, 2007), to the specific, by conducting a laboratory experiment to examine how automation error types affect performance (Dixon,…”
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“60% was chosen as it is below the standard 70% threshold at which human participants begin to be impacted by poorly performing automation [40].”
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“These selected percentages are taken from previous work detailing critical threshold points of reliability-induced automation complacency (Wickens & Dixon, 2007).”
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“Even though perfectly reliable automation rarely exists, the meta-analysis by Wickens and Dixon (2007) indicated that even imperfect automation is beneficial if its reliability reaches 70% .”
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“This accuracy level is based on both performance and trust issues that may arise when the system accuracy level is below ~70% (Lee & See, 2004; Wickens & Dixon, 2007).”
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BackgroundGarcia, M. B. (2013) · doi:10.70675/60049d7czb8acz4099z8966zd3a729d0cba1 · Cited by 1
“Subsequently, Wickens and Dixon (2007), after analyzing a total of 22 studies, concluded that 70% should be the threshold for considering a system as reliable.”
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Masalonis, A., Rein, J. R., Messina, J., & Willems, B. (2013) · doi:10.1177/1541931213571017 · Cited by 1
“Wickens and Dixon (2007), used reliability as the predictor variable in their analysis of how automation performance relates to overall system performance.”
BackgroundMethodInfluentialPredicted Failure Alerting in a Supervisory Control Task Does Not Always Enhance Performance
Gutzwiller, R., Clegg, B. A., Smith, C. A. P., Lewis, J. E., & Patterson, J. D. (2013) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213571079 · Cited by 1
“These accuracy levels were chosen partially based on prior research (Wickens & Dixon, 2007), but also to ensure operators would encounter a suitable amount of failures of both systems during the task.”
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Onnasch, L., Ruff, S., & Manzey, D. (2012) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181312561059 · Cited by 1
“This effect partially supports findings by Wickens and Dixon (2007) who propose a critical reliability cut-off around 70% below which automation support cannot be considered as helpful anymore.”
BackgroundMethodResultInfluentialChen, J. Y. C. (2009) · InTech eBooks · doi:10.5772/6837 · Cited by 1
“Furthermore, Wickens and Dixon (2005) demonstrated that when the reliability level is below approximately 70%, operators often ignore the alerts.”
Designing Trust in Highly Virtual : A Taxonomy of Levels of Autonomy
Galdón, F., Hall, A., & Wang, S. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-61045-6_14 · Cited by 1
Human Factors in Automating Cyber Operations
Gutzwiller, R., & Bruggen, D. V. (2021) · Hawaii International Conference on System Sciences · doi:10.24251/hicss.2021.241 · Cited by 1
“The more automation is making decisions or taking action, the more accurate it needs to be in order for it to actually provide improvement to the system as a whole [27], while automating information acquisition and assessment or analysis stages should be at least 70% accurate [28].”
BackgroundConflict prediction in human-machine systems
Pizziol, S. (2016) · doi:10.70675/2506b2f6z65fdz4f2fz948ez7f92bbceb668 · Cited by 1
Ikejima, S., Ohara, R., Koseki, Y., Narumi, T., & Kuzuoka, H. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3831338
“In G-conditions, ingredient references were swapped with 𝑝 err = .40 probability (e.g., “ Teammate is chopping tomato ” became “ Teammate is chopping lettuce ”), yielding 60% reliability, below the 0.70 threshold at which automation becomes counterproductive [64].”
InfluentialKim, J., & Yang, X. J. (2026) · Human Factors · doi:10.1177/00187208261491447
“The underlying AI classification accuracy was equivalent across the SoA conditions, ranging from 70 to 80%, exceeding the commonly cited 70% perceived usefulness threshold (Wickens & Dixon, 2007).”
Driver Responses to Mismatches Between Expected and Experienced Automated Vehicle Driving Styles
Lee, M., & Pitts, B. J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2739750
Trust in emergency response: The impact of time pressure and artificial intelligence rank
Zou, X., Rau, P. P., Cai, Y., Bai, Z., & Zhang, Y. (2026) · Applied Ergonomics · doi:10.1016/j.apergo.2026.104896
Textor, C., Pak, R., McLaughlin, A. C., & Engle, R. W. (2026) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208261485051
Visual Guidance in Dynamic Search: How Augmented Reality Cue Properties Shape Performance
Warden, A. C., & Zhang, R.-S. (2026) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813261485903
Chen, X., Liu, Y., Ma, Y., Zhang, X., Chen, Y., Wang, Z., Zhang, W., Tian, Y., Gao, Z., & Shen, M. (2026) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208261477486
Munoz, P., Maille, N., Chaudron, L., & Marchand, A. (2026) · Cognition, Technology & Work · doi:10.1007/s10111-026-00892-3
Simon, L., Rauffet, P., & Guerin, C. (2026) · Cognition Technology & Work · doi:10.1007/s10111-026-00890-5 · Cited by 2
Zijlstra, M., & Payne, S. (2026) · Int. J. Hum. Comput. Stud. · doi:10.1016/j.ijhcs.2026.103881
AI Error Difficulty Modulates the Effectiveness of Explainability in Decision Support Systems
Rieger, T., Schindler, H., Koch, K., & Onnasch, L. (2026) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3817603 · Cited by 1
“The system was configured to achieve approximately 88% reliability in order to be perceived as highly reliable while still producing occasional errors that allow the study of overreliance, consistent with prior human factors research on automation reliability [7, 96, 147].”
Song, J., & Wolfe, B. A. (2026) · Cognitive Research Principles and Implications · doi:10.1186/s41235-026-00718-w
“However, our finding in Experiment 2 that participants ignored cues in both high and low reliability conditions runs in contrary to the results of prior studies that found a correlation between performance and reliability (Bliss & Acton, 2003; Wickens & Dixon, 2007).”
Effects of Providing Demonstrations of Robot Reliability
LeMasurier, G., & Yanco, H. A. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3776734.3794425
Evaluating the Effects of Consequences of Error on Trust in AI-Aided Task: An Eye Tracking Study
Kim, J., Kautz, C., & Yang, X. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251358771
“…high consequence stimuli was assessed based on data from 70 participants in previous experiments (J. Kim et al., 2023, 2024 reliability between 70% and 80%, determined based on prior findings that costs tend to outweigh the benefits of automation with reliability below 70% (Wickens & Dixon, 2007).”
Six Guidelines for Trustworthy, Ethical and Responsible Automation Design
Jelínek, M., Schlicker, N., & de Visser, E. (2025) · Pragmatics and Society · doi:10.48550/arxiv.2508.02371
Improving Predictions of Cognitive States for an Adaptive Autonomous System
Rote, N. C., Kintz, J. R., Richardson, E. E., Hayman, A., & Clark, T. K. (2024) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813241277522
“Of the 15 trials each participant completed, the simulated autonomous system offered incorrect solutions in four randomly selected trials to replicate a real-world imperfect autonomous system and elicit realistic trust dynamics (Lee & See, 2004; Wickens & Dixon, 2007).”
McCarley, J. S., Knight, M., & Fletcher, K. I. (2024) · Ergonomics · doi:10.1080/00140139.2024.2386562
“Past work has found that the value of a decision aid tends to decrease with the aid’s reliability. a meta-analysis Wickens and Dixon (2007), for instance, found that aids of reliability below 70% were of little benefit at all to human performance (see also Bartlett et al. in press; Bartlett and…”
Integrated Centralized Framework for Industrial Automation
Mishra, S., & Sealy, W. (2024) · IEEE International Conference on Electro/Information Technology · doi:10.1109/eit60633.2024.10609943
“Each broker instance was configured to handle different topics and Quality of Service (QoS) levels, tailored to the specific data flow and throughput requirements of the industrial components [8].”
BackgroundEvaluation of a Remote Data Collection Method to Study Human-Automation Interaction and Workload
Chandarana, M., Chancey, E. T., Vie, L. R. L., & Politowicz, M. S. (2022) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181322661108
“Research has shown that higher functional reliability leads to higher automation use and, importantly, that there exists a "cross over point" of 70% reliability – below which the human may be better off without the automation (Wickens & Dixon, 2007).”
BackgroundMethodAttentional Control and Performance Across Increasing Degrees of Unreliable Automation
(2020) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181320641294
“While there is active research on the design of such systems to enhance human-automation performance (Hoff and Bashir, 2015; Wickens and Dixon, 2007), there is comparatively less work examining the role of individual differences in explaining human performance variations with automation (however,…”
BackgroundThe Effects of Increasing Degree of Unreliable Automation on Older Adults' Performance
Textor, C., & Pak, R. (2020) · Interacción · doi:10.1007/978-3-030-50232-4_23
Graybeal, J. J., Nguyen, R. T. T., & Bosq, T. W. D. (2019) · Defense + Commercial Sensing · doi:10.1117/12.2518990
The Effect of Stress on Reliance Decisions
McGuire, M. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.030
Reliance on Haptic Assistance Reflected in Haptic Cue Weighting
Gibo, T. L., Plaisier, M., Mugge, W., & Abbink, D. (2019) · IEEE Transactions on Haptics · doi:10.1109/toh.2018.2864278
“Designing for properly calibrated trust and reliance [4] can be achieved by giving information about how the automation works and what its limitations are [2], [5]–[7].”
BackgroundSadler, B. M., Mait, J., Choi, K. K., Nasrabadi, N., Smith, G. L., Pulskamp, J., Sánchez, L. M., Potrepka, D., Proie, R., Ivanov, T., et al. (2018) · Bethlehem University Journal · doi:10.1016/s0140-7007(19)30016-7
Trust Strategies in Consumer Multiple-Component Systems
Lopez, J., & Pak, R. (2018) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931218621397
“60% reliability was chosen for the Living Room location to ensure the user perceives that location as performing unreliably (Wickens & Dixon, 2007).”
MethodTransportation Cyber-Physical Systems: Impacts on Humans
Trapsilawati, F., Hermita, M., & Wickens, C. (2018) · International Conference on Information Control Systems & Technologies · doi:10.1109/icstc.2018.8528696
“Further meta-analysis research [23] found an implication that degree of systems reliability should be maintained to be around 72% to benefit performance.”
BackgroundSelf-citationAttitudes Toward Unreliable Diagnostic Aiding in Dangerous Task Environments
Faerevaag, C. L., Nguyen, B., Jiménez, C., & Jentsch, F. (2017) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601774
“With a reliability rate of only 50%, the unreliable version of SearchBot fell well below the 70% threshold identified by Wickens and Dixon (2007).”
BackgroundMethodInfluentialBenefits and costs of automation support
Onnasch, L. (2015) · DepositOnce · doi:10.14279/depositonce-4407
“How and to what extent operators adjust their own monitoring behaviour in case of the availability of (imperfect) alarm systems or other decision support has been addressed in several studies (e.g. Parasuraman et al., 1993; Wickens and Dixon, 2007).”
InfluentialUnderstanding the Human–Machine System
Hou, M., Banbury, S., & Burns, C. (2014) · CRC Press eBooks · doi:10.1201/b17742-2
From basic to applied research: theory and application of the a–b signal detection theory model
Bustamante, E. A. (2014) · Theoretical Issues in Ergonomics Science · doi:10.1080/1464536x.2011.584581
“Prior research suggests that humans tend to misuse MP systems, while they tend to disuse FP systems (Parasuraman and Riley 1997, Wickens and Dixon 2007).”
Background
Metadata
- Authors
- Wickens, C. D., & Dixon, S. R.
- Corresponding author
- Wickens, Christopher D.
- Venue
- Theoretical Issues in Ergonomics Science
- Publisher
- Informa UK Limited
- Published
- 2007-05
- Volume
- 8
- Issue
- 3
- Pages
- 201-212
- ISSN
- 1463-922X (ISSN-L)
- 1464-536X
- OpenAlex
- W2019160338
- Cited by
- 423
- References
- 0
- Citations by year
- 2026: 31 · 2025: 25 · 2024: 32 · 2023: 26 · 2022: 27 · 2021: 28 · 2020: 29 · 2019: 19 · 2018: 24 · 2017: 18 · 2016: 26 · 2015: 19 · 2014: 22 · 2013: 27 · 2012: 18
- FWCI
- 15.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Theoretical Issues in Ergonomics Science (publishedVersion)
- Funders
- University of Illinois at Urbana-Champaign
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Risk and Safety Analysis
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- automated diagnosis, automation reliability, operator performance, human-automation interaction, workload, crossover point, dual-task performance
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