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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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Cited by
Sorted by citations
Items 201–300 of 408
The Role of Behavioral Anthropomorphism in Human-Automation Trust Calibration
Jensen, T., Khan, M. M. H., & Albayram, Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-50334-5_3 · Cited by 12
The Choice to Use Automation: Improvements from Evidence Accumulation
Patton, C. E., Clegg, B. A., Davis, B. C., Caglar, T., Siebert, C., & Blanchard, N. (2025) · International journal of human computer interactions · doi:10.1080/10447318.2025.2452189 · Cited by 12
“If the automation is highly reliable (accurate) and the operator trusts it, compliance rates tend to go up, such that operators respond affirmatively to an automated system’s recommendation (Du et al., 2020; Huegli et al., 2023; Meyer, 2004; Wickens & Dixon, 2007).”
Situated Interpretation and Data: Explainability to Convey Machine Misalignment
Morey, D. A., & Rayo, M. F. (2024) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2023.3334988 · Cited by 12
“…k -fold cross validation with six folds to obtain model statistics [24], the resulting RF and LR models classified ERT and non-ERT patients with an accuracy of 76% and 70%, respectively, which was at or above the theoretical threshold for automated decision aids to be more helpful than harmful [25].”
MethodResultModeling Trust Dynamics in Human-robot Teaming: A Bayesian Inference Approach
Guo, Y., Zhang, C.-J., & Yang, X. J. (2020) · CHI Extended Abstracts · doi:10.1145/3334480.3383007 · Cited by 12
“The reliability of the robot, and level of automation [10, 11, 14, 15], and environmental r ∈ [0, 1], is defined as the probability that the robot can factors such as multi-tasking requirement [18].”
BackgroundKooij, F. O., Klok, T., Preckel, B., Hollmann, M. W., & Kal, J. E. (2017) · Applied Clinical Informatics · doi:10.4338/aci-2016-08-ra-0138 · Cited by 13
A human factors perspective on the use of automated aids in the evaluation of NDT data
Bertovic, M. (2016) · AIP conference proceedings · doi:10.1063/1.4940449 · Cited by 12
Trustworthiness Evaluation of Multi-sensor Situation Recognition in Transit Surveillance Scenarios
Flammini, F., Marrone, S., Mazzocca, N., Pappalardo, A., Pragliola, C., & Vittorini, V. (2013) · CD-ARES Workshops · doi:10.1007/978-3-642-40588-4_31 · Cited by 12
“Such a wide range of sensors provides a large quantity of heterogeneous information which has to be handled properly, in terms of pre-processing, integration and reasoning, in order to effectively support PSIM operators; otherwise, there is the serious risk of overwhelming operators with unnecessary information, warni…”
BackgroundResultSauer, J., & Chavaillaz, A. (2017) · Applied Ergonomics · doi:10.1016/j.apergo.2016.08.007 · Cited by 12
The Effects of Alarm System Errors on Dependence: Moderated Mediation of Trust With and Without Risk
Chancey, E. T. (2016) · ODU Digital Commons (Old Dominion University) · doi:10.25777/34gr-x929 · Cited by 10
“Wickens and Dixon (2007) published a literature review investigating the effects of signaling system reliability on human performance, specifically reaction time and accuracy.”
BackgroundMethodInfluentialGegoff, I., Tatasciore, M., Bowden, V., & Loft, S. (2025) · Hum. Factors · doi:10.1177/00187208251318465 · Cited by 11
“Taken together, these fi ndings are consistent with the notion that providing recommendations can enhance human decision making (Bartlett & McCarley, 2017, 2021; Wickens & Dixon, 2007).”
Srivastava, D., Lilly, J. M., & Feigh, K. M. (2024) · Cognition Technology & Work · doi:10.1007/s10111-024-00771-9 · Cited by 17
Building a deep learning-based QA system from a CQA dataset
Jin, S., Lian, X., Jung, H., Park, J., & Suh, J. (2023) · Decision Support Systems · doi:10.1016/j.dss.2023.114038 · Cited by 13
A Framework to Explore Proximate Human-Robot Coordination
Matsumoto, S., Washburn, A., & Riek, L. (2022) · ACM Trans. Hum. Robot Interact. · doi:10.1145/3526101 · Cited by 11
“Prior work indicates that people might rely more on automation in a high workload condition [81].”
BackgroundLong, S. K., Lee, J., Yamani, Y., Unverricht, J. R., & Itoh, M. (2022) · Applied Ergonomics · doi:10.1016/j.apergo.2021.103674 · Cited by 11
Meta-analysis of the Effect of Imperfect Alert Automation on System Performance
Rein, J. R., Masalonis, A., Messina, J., & Willems, B. (2013) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213571062 · Cited by 11
“Wickens and Dixon (2007) characterized automation performance using a single metric: reliability, or the total proportion of correct responses: (1) where H = total number of hits, CR = correct rejections, M = misses, and FA = false alerts.”
BackgroundMethodResultInfluentialMental effort in binary categorization aided by binary cues.
Botzer, A., Meyer, J., & Parmet, Y. (2013) · Journal of experimental psychology. Applied · doi:10.1037/a0031625 · Cited by 11
The Influence of Perceived Task Difficulty and Importance on Automation Use
Schwark, J. D., Dolgov, I., Graves, W. J., & Hor, D. (2010) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193121005401931 · Cited by 11
“Other benefits of automation include improving task performance and reducing operator workload (Wickens & Dixon, 2007).”
BackgroundA Schema of Possible Negative Effects of Advanced Driver Assistant Systems
Mahr, A., & Müller, C. A. (2017) · International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (Driving Assessment Conference) · doi:10.17077/drivingassessment.1386 · Cited by 10
“…al., 1997), as previous research has stated that false alarms could be more harmful to a user’s performance than miss rate (Bliss, 2003; Dixon et al., 2006; Wickens & Dixon, 2007), while on the other hand a certain level of miss rate is tolerable, especially in demanding multi-tasking situations.”
Altering user’ acceptance of automation through prior automation exposure
Bekier, M., & Molesworth, B. (2017) · Ergonomics · doi:10.1080/00140139.2016.1216610 · Cited by 10
“According to Muir and colleagues, user acceptance of automation is highly dependent on many factors, one of which is trust that the technology will function as expected (Lee and See 2004; Muir 1987, 1994; Wickens and dixon 2007).”
BackgroundHighlighting items as means of adaptive assistance
Antwarg, L., Lavie, T., Rokach, L., Shapira, B., & Meyer, J. (2013) · Behavior and Information Technology · doi:10.1080/0144929x.2011.650710 · Cited by 10
“When an automated system is not completely reliable the automation risks being useless, harming performance (Wickens and Dixon 2007), decreasing user trust in the system and even causing users to ignore suggestions completely or to disable the suggestions offered by the system.”
BackgroundSchuster, D. M., Jentsch, F. G., Fincannon, T. D., & Ososky, S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39360-0_28 · Cited by 7
Buchner, S., Kintz, J. R., Zhang, J. Y., Banerjee, N. T., Clark, T., & Hayman, A. (2025) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434241310084 · Cited by 9
“…ratio was motivated by other studies using autonomous decision aid reliability in the 60% – 90% range (Akash et al., 2020; Nunnally, 1978; Petersen et al., 2019; Wickens & Dixon, 2007; Wickens et al., 2020), which has been shown to produce realistic trust dynamics in a short experiment time frame.”
When Do Humans Heed AI Agents’ Advice? When Should They?
Dunning, R. E., Fischhoff, B., & Davis, A. (2023) · Hum. Factors · doi:10.1177/00187208231190459 · Cited by 9
“Probabilities may have little value, though, if a system has such great predictive value that humans just defer to it or such little predictive value that humans stop using it (Meyer, 2004; Parasuraman & Riley, 1997; Wickens & Dixon, 2007).”
BackgroundReturn-to-Manual Performance can be Predicted Before Automation Fails
Griffiths, N., Bowden, V., Wee, S., & Loft, S. (2022) · Hum. Factors · doi:10.1177/00187208221147105 · Cited by 9
“Automation functioned reliably for 24 out of 30 con fl icts, with participants required to manually intervene to the six automation failures (80% automation reliability to ensure that reliability was high enough to encourage individuals to rely on automated advice; Wickens & Dixon, 2007).”
MethodEnhancing component-specific trust with consumer automated systems through humanness design
Lopez, J., Watkins, H., & Pak, R. (2022) · Ergonomics · doi:10.1080/00140139.2022.2079728 · Cited by 9
“We chose 60% reliability because systems performing below 70% reliability are considered unreliable (Wickens and Dixon 2007; Kantowitz, Hanowski, and Kantowitz 1997).”
A Closer Look at Warning Cues on the Sustained Attention to Response Task Performance
Dang, J. A., Shaw, T. H., McKnight, P. E., & Helton, W. (2022) · Hum. Factors · doi:10.1177/00187208211060708 · Cited by 9
“Their fi ndings regarding performance were aligned with earlier studies in which more reliable automation systems improve performance, but low reliability systems may not be bene fi cial to performance when compared to no-automation control conditions (Wickens & Dixon, 2007).”
BackgroundEvent-Related Potential Responses of Motorcyclists Towards Rear End Collision Warning System
Muzammel, M., Yusoff, M., & Mériaudeau, F. (2018) · IEEE Access · doi:10.1109/access.2018.2845899 · Cited by 9
“Both imperfect warning systems improved the motorcyclist performance compared to the non-warning system and it seems to facilitate the sensory processing of the target and results are in line with those from [37] and [53].”
ResultPredicting the effects of automation reliability rates on human-automation team performance
Hillesheim, A. J., & Rusnock, C. F. (2016) · Online World Conference on Soft Computing in Industrial Applications · doi:10.1109/wsc.2016.7822227 · Cited by 9
“While studies have examined the use of automation reliability as an aid to the user (Dixon and Wickens 2006; Wickens and Dixon 2007; Parasuraman and Manzey 2010) there is a lack of depth in the literature that addresses when an automated agent, who acts like a teammate rather than an aid, suffers…”
BackgroundInfluentialEffects of Alarm Modality and Alarm Reliability on Workload, Trust, and Driving Performance
Shah, S., Bliss, J. P., Chancey, E. T., & Brill, J. C. (2015) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931215591332 · Cited by 8
“We chose 70% reliability level based on the Wickens and Dixon (2007) study which reported that for imperfect automation systems to be effective; the systems should at least be 70% reliable.”
MethodThe Contribution of Automation to Resilience in Rail Traffic Control
Ferreira, P., & Balfe, N. (2014) · Interacción · doi:10.1007/978-3-319-07515-0_46 · Cited by 9
A robust approach for on-line and off-line threat detection based on event tree similarity analysis
Flammini, F., Pragliola, C., Pappalardo, A., & Vittorini, V. (2011) · Advanced Video and Signal Based Surveillance · doi:10.1109/avss.2011.6027364 · Cited by 9
“In reference to the case of joint automation-human performance, a research revealed that alarms systems should have a reliability factor of 70% [ 15 ].”
BackgroundMahr, A., Cao, Y.-J., Theune, M., Dimitrova-Krause, V., Schwartz, T., & Müller, C. A. (2010) · European Conference on Artificial Intelligence · doi:10.3233/978-1-60750-606-5-1051 · Cited by 9
“be more harmful to the user’s performance than MISS [1, 4, 10].”
BackgroundInfluentialExplaining AI weaknesses improves human–AI performance in a dynamic control task
Rieger, T., Schindler, H., Onnasch, L., & Roesler, E. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103505 · Cited by 13
Benchmarking automation-aided performance in a forensic face matching task
Bartlett, M. L., Carragher, D. J., Hancock, P., & McCarley, J. S. (2024) · Applied Ergonomics · doi:10.1016/j.apergo.2024.104364 · Cited by 9
Agbanyo, G. K., & Shi, M.-Y. (2024) · Sustainability · doi:10.3390/su16083302 · Cited by 8
Situated Visual Alarm Displays Support Machine Fitness Assessment for Nonexplainable Automation
Rayo, M. F., Horwood, C. R., Fitzgerald, M. C., Grayson, M. R., Abdel-Rasoul, M., & Moffatt-Bruce, S. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3155714 · Cited by 8
Trapsilawati, F., Chen, C.-H., Wickens, C., & Qu, X.-D. (2021) · Journal of navigation · doi:10.1017/s0373463320000703 · Cited by 8
“…et al. (2015, 2016a) nevertheless found that such an imperfect (80% reliable) CRA still supported ATCO conflict avoidance performance, well above the level of unaided manual task performance, and similar findings have been observed with other imperfect automation aids (Wickens and Dixon, 2007).”
BackgroundResultSelf-citationGaldón, F., & Wang, S. (2019) · International Conference on Human Interaction and Emerging Technologies · doi:10.1007/978-3-030-25629-6_7 · Cited by 8
Geuss, M. N., Larkin, G. B., Swoboda, J. C., Yu, A. B., Bakdash, J. Z., White, T., Berthiaume, M., & Lance, B. J. (2019) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.2518405 · Cited by 4
Cognitive status and repetitive working tasks of low risk
Giagloglou, E., Mijović, P., Branković, S., Antoniou, P., & Mačužić, I. (2017) · Safety Science · doi:10.1016/j.ssci.2017.10.004 · Cited by 8
Dolgov, I., & Kaltenbach, E. K. (2017) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601799 · Cited by 8
“Well-designed automation generally reduces the effort that a person needs to expend and improves task performance (e.g., Wickens & Dixon, 2007).”
BackgroundCapturing Performance in Cyber Human Supervisory Control
Mancuso, V. F., Funke, G., Strang, A., & Eckold, M. B. (2015) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931215591066 · Cited by 8
“…an important consideration is operator span-of control, i.e., the number of assets that a human is able to operate at any given point in time (e.g. Crandall et al., 2005; Cummings & Guerlain, 2004; Endsley & Rodgers, 1997; Dixon et al., 2005; Wickens & Dixon, 2007; Wickens et. al, 2013).”
BackgroundDrivers’ Reliance on Lane-Keeping Assistance Systems as a Function of the Level of Assistance
Popken, A., Krems, J., & Nilsson, L. (2015) · Zeitschrift für Arbeitswissenschaft · doi:10.1007/bf03373945 · Cited by 8
An Expansion of System Wide Trust Theory Using In-Vehicle Automation
Blair, K., Sandry, J., & Rice, S. C. (2012) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181312561336 · Cited by 8
“These demands can result in decreased efficiency and safety (Maltz & Shinar, 2003; Sheridan, 1997; Wickens & Dixon, 2007).”
BackgroundContext conditions drivers' disposition towards alarms
Lees, M. N. (2010) · doi:10.17077/etd.188inyqs · Cited by 8
“Wickens and Dixon (2007) used data from 20 studies to identify the value at which automation (relative to performance when no automation is present) fails to benefit the operator or becomes a drawback because of its level of reliability and whether such a value is influenced by task workload.”
BackgroundValidation of a new method for designing air traffic control alarms
Ruskin, K., & Ruskin, A. C. (2023) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2023.100965 · Cited by 7
Morey, D. A., Vella, D. D., Rayo, M. F., Zelik, D. J., & Murphy, T. (2022) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181322661537 · Cited by 7
Pampel, S. M., Southey, T. J. R., & Burnett, G. (2020) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2019.0673 · Cited by 6
Chancey, E. T., Yamani, Y., Brill, J. C., & Bliss, J. P. (2017) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601890 · Cited by 7
“Generally, higher reliability leads to higher signal dependence rates (Manzey, Gerard, & Wiczorek, 2014), quicker signal response times (Chancey, Bliss, Proaps, & Madhavan, 2015), and greater user sensitivity (Rice, 2009) (for review, see Wickens & Dixon, 2007).”
BackgroundChancey, E. T., Bliss, J. P., Liechty, M. M., & Proaps, A. B. (2015) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931215591141 · Cited by 7
“In an attempt to elicit measurable differences in response behaviors, we elected reliability levels above and below a 71% cutoff (see Wickens & Dixon, 2007, for meta-analysis).”
MethodEffects of Age and Gender Stereotypes on Trust in an Anthropomorphic Decision Aid
Bass, B., Goodwin, M., Brennan, K., Pak, R., & McLaughlin, A. (2013) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213571351 · Cited by 7
“That is, when automation is much less than 70% reliable, the operator begins to behave as if there was no automation present (Wickens & Dixon, 2007).”
BackgroundMcCarley, J. (2009) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120905301713 · Cited by 7
Understanding Automation Failure
Endsley, M. (2024) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434231222059 · Cited by 6
“This is more likely with automation that is more reliable which acts to increase trust (Wickens & Dixon, 2007).”
Müller, R., Reindel, D. F., & Stadtfeld, Y. D. (2023) · Human Factors and Ergonomics in Manufacturing & Service Industries · doi:10.1002/hfm.21032 · Cited by 6
“| 411 literature, with an accuracy below 70% automated aids tend to lose their benefits, and aided performance drops below the level of manual performance (Wickens & Dixon, 2007).”
Cockram, L., Bartlett, M. L., & McCarley, J. S. (2023) · Applied Ergonomics · doi:10.1016/j.apergo.2023.104027 · Cited by 7
Rodríguez, S., Zaroukian, E. G., Hoye, J., & Asher, D. E. (2022) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434221129166 · Cited by 10
“…of the AI system (e.g., by making it non-functional or non-responsive), we trivialize the proposed research questions, as prior work has widely addressed the relationship between faulty systems and subjective attitudes (Parasuraman, 1997; Parasuraman et al., 1993; Wickens & Dixon, 2007).”
Lee, B. C., Park, J., Jeong, H., & Park, J. (2020) · Applied Sciences · doi:10.3390/app10041288 · Cited by 6
Modeling Relationships among Workload, Trust, and Visual Scanning in an Automated Flight Task
Karpinsky, N. D., Chancey, E. T., & Yamani, Y. (2016) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601356 · Cited by 6
“The FAprone or miss-prone system operated at 70% reliability, at which point operators may still depend upon the automation even though their own unaided performance would likely be superior (Wickens & Dixon, 2007).”
BackgroundCullen, R. H., Dan, C.-S., Rogers, W., & Fisk, A. (2014) · International journal of human computer interactions · doi:10.1080/10447318.2014.906158 · Cited by 6
“This stands in contrast with the results of previous studies in performance, where 67% reliability was shown to neither hinder nor help (Wickens & Dixon, 2007), although this study was measuring attention allocation and not task performance.”
BackgroundResultAre the Intrusive Effects of SPAM Probes Present When Operators Differ by Skill Level and Training?
Silva, H. I., Ziccardi, J., Grigoleit, T., Battiste, V., Strybel, T., & Vu, K.-P. L. (2013) · Interacción · doi:10.1007/978-3-642-39209-2_31 · Cited by 6
“With highly automated tasks, the operator is less vigilant, and, as a result, will have a harder time regaining awareness should something go wrong [3-4].”
BackgroundBayesian and Signal Detection Models
McCarley, J., & Benjamin, A. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199757183.013.0032 · Cited by 6
“Unfortunately, empirical performance of automation-aided humans is rarely if ever optimal (Parasuraman & Riley, 1997) and often fails to even match the performance of the automation by itself (see Wickens & Dixon, 2007, for review).”
BackgroundEffects of unreliable automation in scheduling operator attention for multi-robot control
Chien, S.-Y., Lewis, M., Mehrotra, S., & Sycara, K. (2012) · IEEE International Conference on Systems, Man and Cybernetics · doi:10.1109/icsmc.2012.6377721 · Cited by 6
“Although imperfect automation might still contribute to overall performance, [17] concluded that when the reliability level is less than 70%, the imperfect automation would be even worse than no automated aids.”
BackgroundReliability of a Cued Combat Identification Aid on Soldier Performance and Trust
Chancey, E. T., & Bliss, J. P. (2012) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181312561411 · Cited by 6
“We hypothesized that the more reliable the CID aid is the more accurate the Soldier would be at making friend-foeidentifications via target shot choice (Wang et al., 2009, Wickens & Dixon, 2007).”
BackgroundConfluence theory: uniting two houses divided
Rice, S. C., Trafimow, D., Keller, D., & Bean, N. (2011) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2010.525760 · Cited by 6
“…the latter two stages (e.g. Sarter and Schroeder 2001, Rovira et al. 2007), most studies have focused on the early two stages (e.g. Horrey and Wickens 2001, Yeh and Wickens 2001), with particular focus on diagnostic automation (e.g. Dixon et al. 2007, Wickens and Dixon 2007, Rice and Keller 2009).”
BackgroundLove, B. C., Jones, M., Tomlinson, M. T., & Howe, M. (2009) · International Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518905 · Cited by 6
“Finally, RADAR can be incrementally deployed with increasing rates of automation over time in order to maximize the benefits of automation and minimize the detriments [39].”
MethodWang, S., Liu, Y., Wang, X., Liu, Z., You, X., & Li, Y. (2024) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208241311808 · Cited by 6
“The effect of automation reliability on task performance has been examined in many studies (Onnasch, 2015; Rovira et al., 2002, 2007; Wickens & Dixon, 2007), which can be classi fi ed as either studies on failure performance or studies on joint human-automation performance (i.e., the combination of…”
Human vs. Automation: Which One Will You Trust More If You Are About to Lose Money?
Fahim, M., Khan, M. M. H., Jensen, T., & Albayram, Y. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2076772 · Cited by 5
“Prior work suggests that the reliability below 70% is considered worse than no automation (Wickens & Dixon, 2007).”
BackgroundThe Mediating Effect of Emotions on Trust in the Context of Automated System Usage
Fahim, M., Khan, M. M. H., Jensen, T., Albayram, Y., Coman, E., & Buck, R. W. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3094883 · Cited by 5
“Prior efforts have suggested that automation reliability below 70% is considered worse than no automation at all [52].”
BackgroundFahim, M. A. A., Khan, M. M. H., Jensen, T., Albayram, Y., Coman, E. N., & Buck, R. (2019) · International Conference on Multimodal Interaction · doi:10.1145/3340555.3353732 · Cited by 5
“Prior work has suggested that automation with reliability below 70% is considered worse than no automation [53].”
BackgroundInfluence of trust in Ambient Assisted Living technologies
Steinke, F. (2015) · doi:10.18452/17167 · Cited by 5
“In previous studies, reliability levels between 60% and 80% represented a threshold of performance decline (Rovira et al. 2007; Wickens and Dixon, 2005).”
BackgroundEffects of Imperfect Automation and Task Load on Human Supervision of Multiple Uninhabited Vehicles
de Visser, E., & Parasuraman, R. (2007) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120705101806 · Cited by 5
“Despite the costs of automation unreliability, studies have also shown that imperfect automation can still yield system performance benefits above baseline performance (Wickens & Dixon, 2005).”
BackgroundLee, M., & Pitts, B. J. (2026) · Safety Science · doi:10.1016/j.ssci.2025.107062 · Cited by 4
Narayanan, R., & Feigh, K. M. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2618568 · Cited by 5
“A well-known crossover point beyond which automation is deemed unreliable is 70% (Wickens & Dixon, 2007).”
Bahnsen, K. L., Dischinger, E., & Grundgeiger, T. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713685 · Cited by 4
“Research indicates that the benefits of automated processes decrease when reliability is below approximately 70% [72].”
InfluentialSelective Access to Decision Support as a Function of Event Uncertainty
Ben-Yaakov, Y., McCarley, J. S., & Meyer, J. (2024) · Hum. Factors · doi:10.1177/00187208241277158 · Cited by 4
“However, DS is not guaranteed to be helpful (Parasuraman & Manzey, 2010) and can even compromise performance (Wickens & Dixon, 2007).”
Lee, M., & Pitts, B. J. (2024) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813241260388 · Cited by 4
“This reliability level was chosen to acknowledge previous research findings indicating that people’s performance with automation that is less than 70% reliable could be worse than with no automation at all (Wickens & Dixon, 2007).”
I Spy Differences in System Reliability
Patton, C. E., Warden, A. C., & Pharmer, R. L. (2023) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/21695067231192718 · Cited by 4
Hadler, R. A., Dexter, F., & Epstein, R. (2023) · Cureus · doi:10.7759/cureus.34913 · Cited by 4
“Note that our use of 80% is not meant to suggest clinical or managerial relevance to applying a threshold probability >80%, because we doubt its relevance for that purpose [9,10].”
BackgroundSymbiotic Design Application in Healthcare: Preventing Hospital Acquired Infections
Li, M., Morey, D. A., & Rayo, M. F. (2021) · Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare. International Symposium of Human Factors and Ergonomics in Healthcare · doi:10.1177/2327857921101138 · Cited by 4
“…interact can produce outsized positive (Corcoran et al., 1972; Parasuraman, 1987; Thackray & Touchstone, 1989; Woods & Hollnagel, 2006) or negative (Robinson & Sorkin, 1985; Sorkin & Woods, 1985; Dalal & Kasper, 1994; Smith et al., 1997; Wickens & Dixon, 2007) effects on the system as a whole.”
BackgroundKneeland, C. M., Houpt, J. W., & Bennett, K. B. (2021) · Computational Brain & Behavior · doi:10.1007/s42113-021-00104-3 · Cited by 4
“Researchers have speculated various factors that influence the human ’ s use of the automation including the display design (e.g., Yamani & McCarley, 2018) and aid ’ s reliability (e.g., Wickens & Dixon, 2007).”
BackgroundAn Investigation of Trust in a Cyber Security Tool
Brown, P., Christensen, K. K., & Schuster, D. M. (2016) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601333 · Cited by 5
“Thus, it functioned as diagnostic aid (Wickens & Dixon, 2007).”
BackgroundPre-study Walkthrough with a Commercial Pilot for a Preliminary Single Pilot Operations Experiment
O'Connor, R., Roberts, Z. S., Ziccardi, J., Koteskey, R. W., Lachter, J., Dao, A.-Q. V., Johnson, W., Battiste, V., Vu, K.-P. L., & Strybel, T. (2013) · Interacción · doi:10.1007/978-3-642-39215-3_17 · Cited by 4
“Automation typically lowers operators’ overall workload [4], and in mundane conditions, automation can maintain more consistent and accurate performance than that obtained by human operators.”
BackgroundDevelopment of an Initial Model of Human-Automatio
Oxstrand, J., O'Hara, J., Joe, J. C., & Kat (2013) · doi:10.2172/1084681 · Cited by 4
“Wickens and Dixon (2007) reviewed the findings of 20 studies that used automation’s reliability as an independent variable to quantify its effects on task performance.”
BackgroundResultChiu, S.-D., Cullen, R. H., Rogers, W., & Fisk, A. (2012) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181312561450 · Cited by 4
“Previous research also showed that for the automated alert that directs attention allocation, reliability higher than 67% would benefit operators than the cost of errors (Wickens & Dixon, 2007).”
BackgroundThe Effects of Automation Reliability and Experience on Attention in a Computer Environment
Cullen, R. H., Rogers, W., & Fisk, A. (2012) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181312561106 · Cited by 4
“At 67% reliability, where Wickens and Dixon (2007) suggest automation neither helps nor hinders performance, we expected a similar level of allocation behaviors to those without any automation support.”
BackgroundResultBalancing costs and benefits of automated task allocation in mobile surveillance
Streefkerk, J. W., Esch-Bussemakers, M. P. V., & Neerincx, M. A. (2010) · European Conference on Cognitive Ergonomics · doi:10.1145/1962300.1962321 · Cited by 4
“These errors decrease task performance and user satisfaction, and causes decision errors and complacency effects (Gajos, Everitt, Tan, Czerwinski, & Weld, 2008; Rovira, McGarry, & Parasuraman, 2007; Wickens et al., 2007).”
BackgroundSome cues are more equal than others: Cue plausibility for false alarms in baggage screening.
Chavaillaz, A., Schwaninger, A., Michel, S., & Sauer, J. (2019) · Applied Ergonomics · doi:10.1016/j.apergo.2019.102916 · Cited by 4
Trust and Complacency in Cyber Security
Cain, A. A. (2016) · San José State University ScholarWorks (San Jose State University) · doi:10.31979/etd.a4nf-q57u · Cited by 4
“The researcher selected these reliability levels because 60% reliability is the lowest level of reliability at which automation is still useful (Wickens & Dixon, 2007), and 97.25% reliability represents a high reliability level while accounting for the fact that automation is imperfect.”
BackgroundInfluentialHow do automation false alarms and misses affect human trust in automated driving?
Li, J., Liu, J., & Liu, L. (2026) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2026.103939 · Cited by 3
Lee, M., & Pitts, B. J. (2026) · Accident Analysis and Prevention · doi:10.1016/j.aap.2026.108412 · Cited by 3
Sun, J., Yu, S.-H., Chu, J.-J., Xie, X., Cun, W., & Fan, H. (2025) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2025.103849 · Cited by 3
Zhang, H.-S., Zhou, R., Cheng, C., Huang, S.-H., Cheng, M., & Yang, C.-T. (2025) · Cognitive Research · doi:10.1186/s41235-025-00659-w · Cited by 3
“Indeed, when reliability falls below a critical threshold (in the range of 70–75%), automation may lead to increased errors and inefficiencies, perhaps yielding inferior performance compared to the absence of automation (Wickens & Dixon, 2007).”
Filtering Out the User?: Are Users Complacent with Phishing Emails Due to Automated Filters?
Sarno, D. M., Gendron, C., & Volante, W. G. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2429759 · Cited by 2
“…filter acted with high reliability (87.5% accuracy), and half were told it acted with low reliability (56.25% accuracy) - past research has demonstrated that 70% reliability is the minimum threshold for automated aids (e.g., Parasuraman & Manzey, 2010; Rovira et al., 2007; Wickens & Dixon, 2007).”
InfluentialPredicting Trust Dynamics Type Using Seven Personal Characteristics
Chung, H., & Yang, X. J. (2024) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2025.3627578 · Cited by 3
“Theoretically, two metaanalyses by Wickens and Dixon [15] and Rein et al. [16] have shown that automation with reliability below 70% can still yield performance gains, suggesting the need for investigation into trust in this lower reliability range.”
Evaluating the Influence of Incorrect Reassurances on Trust in Imperfect Automated Decision Aid
Kim, J., Richie, O., & Yang, X. J. (2024) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813241264498 · Cited by 3
“The reliability followed the suggestion of synthesizing literature that if automation performance falls below 70% reliability, the costs will outweigh the benefits (Wickens & Dixon, 2007).”
Zhang, T.-M., Ren, J.-D., Sun, M., & Zheng, H. (2024) · Human Factors and Ergonomics in Manufacturing & Service Industries · doi:10.1002/hfm.21047 · Cited by 3
“In situations where automation has not reached an adequate level, its control or operation may not yield better results than manual operation, particularly in circumstances that require logical judgment before deciding how to control or operate (Balfe et al., 2015; Wickens & Dixon, 2007).”
Morey, D. A., Gifford, R. C., & Rayo, M. F. (2024) · Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care · doi:10.1177/2327857924131046 · Cited by 3
“This paper outlines one human-centered machine learning (HCML) approach which deliberately invested in the potential for human-machine teaming (HMT) in all phases.”
InfluentialCollaborative Intelligent Industrial Robots (CIIR) Framework
Alremeithi, K., Giri, P., & Sealy, W. (2023) · 2023 International Conference on Electrical, Communication and Computer Engineering (ICECCE) · doi:10.1109/icecce61019.2023.10442250 · Cited by 3
“This shift not only allocates valuable resources for strategic investment but also equips SMEs to maintain competitiveness in the dynamic market landscape, swiftly adapting to evolving technologies and processes [3], [4].”
BackgroundPredicting Operator Cognitive States for Supervisory Human-Autonomy Teaming
Kintz, J. R., Buchner, S., Anderson, A. P., & Clark, T. (2023) · IEEE International Conference on Systems, Man and Cybernetics · doi:10.1109/smc53992.2023.10394254 · Cited by 3
“This ratio of autonomous system failure (4 of 15) aimed to replicate realistic trust dynamics in a short experiment, with a system that was useful despite its errors [11], [28], [52], [53].”
BackgroundChanges in Trust in Automation (TIA) After Performing a Visual Search Task with an Automated System
Driggs, J. B., & Vangsness, L. (2022) · International Conferences on Human-Machine Systems · doi:10.1109/ichms56717.2022.9980724 · Cited by 3
“Moreover, our finding is consistent with past research which suggests an automated system reliability of 70% as the threshold above which users perceive an automated system to be reliable [28].”
Result
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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