The DOI matches its Crossref record; 78 of the 79 references it cites were found in a registry or an index.
Automation bias: a systematic review of frequency, effect mediators, and mitigators
Open accessAbstract
Automation bias (AB)--the tendency to over-rely on automation--has been studied in various academic fields. Clinical decision support systems (CDSS) aim to benefit the clinical decision-making process. Although most research shows overall improved performance with use, there is often a failure to recognize the new errors that CDSS can introduce. With a focus on healthcare, a systematic review of the literature from a variety of research fields has been carried out, assessing the frequency and severity of AB, the effect mediators, and interventions potentially mitigating this effect. This is discussed alongside automation-induced complacency, or insufficient monitoring of automation output. A mix of subject specific and freetext terms around the themes of automation, human-automation interaction, and task performance and error were used to search article databases. Of 13 821 retrieved papers, 74 met the inclusion criteria. User factors such as cognitive style, decision support systems (DSS), and task specific experience mediated AB, as did attitudinal driving factors such as trust and confidence. Environmental mediators included workload, task complexity, and time constraint, which pressurized cognitive resources. Mitigators of AB included implementation factors such as training and emphasizing user accountability, and DSS design factors such as the position of advice on the screen, updated confidence levels attached to DSS output, and the provision of information versus recommendation. By uncovering the mechanisms by which AB operates, this review aims to help optimize the clinical decision-making process for CDSS developers and healthcare practitioners.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The result is a cognitive moral hazard: epistemic responsibility is abdicated not because delegation is appropriate, but because the trainee cannot distinguish appropriate delegation from uncritical acceptance 29 .”
From AI-induced never-skilling in medical education (2026) · cites this paper as [29]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Automation bias, well described in clinical decision support contexts, can lead individuals to privilege automated recommendations even when flawed, contributing to errors of commission or omission and reducing vigilance in information seeking [47].”
From Design and evaluation of a faculty development workshop series on integrating generative artificial intelligence in medical education: mixed methods pilot study (2026) · cites this paper as [47]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For humans, post-hoc interaction with such outputs is opaque and coarse-grained: experts must reverseengineer the model's reasoning to correct errors, while novices risk automation bias and over-reliance on machine-generated authority [32, 44, 87].”
From Criticmate: Stagewise Human–AI Co-Critique in Single-Screen UI Evaluation (2026) · cites this paper as [32]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These fi ndings relate to work on automation bias, algorithm aversion, and 'appropriate reliance' [5, 32, 44, 87].”
From Criticmate: Stagewise Human–AI Co-Critique in Single-Screen UI Evaluation (2026) · cites this paper as [32]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“HR professionals might utilize a system for decision support differently, as prior experience with personnel selection tasks is related to higher confidence in one’s own decisions, resulting in a lower reliance on the system and less automation bias (Goddard et al., 2012; Langer et al., 2021).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This way, the design of the system can support the attention of decision makers by varying reliability over time (Goddard et al., 2012; Zerilli et al., 2019).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Automation bias has been found to be a serious problem in contexts of AI-based decision support systems (Mosier and Skitka, 1999; Goddard et al., 2012; Lyell et al., 2018; Davis et al., 2020)”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The core finding is broadly consistent: when humans receive an algorithmic recommendation, they defer to it at rates exceeding what would be warranted by the algorithm's demonstrated accuracy, even when available evidence contradicts the recommendation (Skitka, Mosier, and Burdick 1999;Goddard, Roudsari, and Wyatt 2012).”
From Limits of Interventions in AI-Assisted Decision Making (2026) · cites this paper as Goddard, Roudsari, and Wyatt 2012
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“These factors have also been deemed influential in a systematic review by Goddard et al. (2012).”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Goddard et al., 2012
The cited source says what the claim attributes to it.
In the citing paper
“The risk of automation bias occurs in this process: a human being relies too heavily on the algorithm, even in the face of erroneous predictions (Goddard et al. 2011).”
From Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response (2022) · cites this paper as Goddard et al. 2011
The cited source says what the claim attributes to it.
In the citing paper
“In the conclusion of their systematic review, Goddard et al. (2012) propose that expertise may act on primary drivers to influence reliance on DSS, with those primary drivers being trust and confidence, again emphasising that expertise likely plays an indirect role on reliance behaviour.”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Goddard et al., 2012
The cited source says what the claim attributes to it.
In the citing paper
“Expertise, expected to be a predictor of both trust and reliance (Goddard et al., 2012), has been shown to reduce trust and reliance on AI, even when the user's judgement is incorrect (Küper et al., 2024).”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Goddard et al., 2012
The cited source says what the claim attributes to it.
In the citing paper
“Goddard et al. (2012) propose that heightened confidence in one's own decisions generally diminishes reliance on external support, identifying confidence as a primary influence on reliance behaviour alongside trust.”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Goddard et al., 2012
The cited source says what the claim attributes to it.
In the citing paper
“This risk is amplified by automation bias, the tendency to over-rely on algorithmic output, by leading clinicians to accept erroneous recommendations without adequate scrutiny.17-21”
From Automation Bias in Large Language Model Assisted Diagnostic Reasoning Among AI-Trained Physicians (2025) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“The principle of non-discrimination in algorithmic decision-making is the last to consider for completing the algorithmic legality fundamental requirements. This translates into a duty to avoid so-called automation biases 75 , preventing outcomes that might be discriminatory toward the addressee of administrative action.”
From Transparency Warranties for Algorithmic Decisions in Administrative Procedures. Synopsis between EU Law and the Italian Legal System (2026) · cites this paper as Goddard K, Roudsari A, Wyatt JC (2012)
The cited source says what the claim attributes to it.
In the citing paper
“Experimental lab studies have diagnosed this tendency across a number of research fields (Goddard, Roudsari, and Wyatt 2012).”
From Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice (2023) · cites this paper as Goddard, Roudsari, and Wyatt 2012
The cited source says what the claim attributes to it.
In the citing paper
“However, systems to aid decision-making might especially be considered for novice HR professionals as these systems tend to improve the decision-making quality of less experienced decision makers (Goddard et al., 2012; Langer et al., 2021).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
The cited source says what the claim attributes to it.
In the citing paper
“Automation bias has been found to lead to adverse effects on decision outcomes in several other contexts before (e.g., Parasuraman and Manzey, 2010; Goddard et al., 2012).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
The cited source says what the claim attributes to it.
In the citing paper
“Even when AI operates at the level of recommendation rather than direct action, the mere presence of an algorithmic suggestion can systematically shift human judgment toward the algorithm's conclusion, a phenomenon known as automation bias (Goddard, Roudsari, and Wyatt 2012;Alon-Barkat and Busuioc 2023).”
From Limits of Interventions in AI-Assisted Decision Making (2026) · cites this paper as Goddard, Roudsari, and Wyatt 2012
The cited source says what the claim attributes to it.
In the citing paper
“Human involvement therefore does not necessarily guarantee effective control (Goddard et al. 2012).”
From When Recruiters Meet Algorithms: Advice Taking and Bias in AI‐Assisted Recruitment (2026) · cites this paper as Goddard et al. 2012
The cited source says what the claim attributes to it.
In the citing paper
“This tendency is consistent with automation bias, in which people overrely on automated recommendations and accept them without sufficient independent verification (Goddard et al., 2012).”
From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Goddard et al., 2012
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“All had previously passed our standard psychological screener [30].”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [30]
Experimental — accuracy not yet measuredIn the citing paper
“Automation bias can occur in busy clinical settings where clinicians may feel pressure to make rapid decisions, leading them to rely on AI-generated results as a shortcut rather than engaging in thorough analysis.44”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [44]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
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
Garg, A. X., Adhikari, N. K. J., McDonald, H., Rosas-Arellano, M. P., Devereaux, P. J., Beyene, J., Sam, J., & Haynes, R. B. (2005) · JAMA · doi:10.1001/jama.293.10.1223 · Cited by 2,936
Role of Computerized Physician Order Entry Systems in Facilitating Medication Errors
Koppel, R. (2005) · JAMA · doi:10.1001/jama.293.10.1197 · Cited by 2,126
The Hazards of Scoring the Quality of Clinical Trials for Meta-analysis
Jüni, P. (1999) · JAMA · doi:10.1001/jama.282.11.1054 · Cited by 1,981
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
Trust, control strategies and allocation of function in human-machine systems
LEE, J., & MORAY, N. (1992) · Ergonomics · doi:10.1080/00140139208967392 · Cited by 1,459
Research works printed but not linked: 2
- Wyatt J . Lessons learned from the field trial of ACORN, an expert system to advise on chest pain. In: Barber B Cao D Qin D , eds. Proc. Sixth World Conference on Medical Informatics, Singapore. Amsterdam: North Holland, 1989:111–15.
- Prinzel LJ Freeman FG Prinzel HD . Individual differences in complacency and monitoring for automation failures. Individual Differences Research 2005;3:27–49.
Cited by
Sorted by citations
Items 601–700 of 1,008
Knauer, R., Kalmring, M., & Rodner, E. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1789412 · Cited by 1
Engineering framework for curiosity-driven and humble AI in clinical decision support
Arslan, J., Benke, K. K., Cajas, S. A., Castro, R., Celi, L. A., Cruz-Suarez, G. A., Reyes, R. D., Engelmann, J., Ercole, A., Hilel, A., et al. (2026) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2025-101877 · Cited by 1
Venerito, V., Lopalco, G., Iannone, F., & Gupta, L. (2026) · The Lancet Rheumatology · doi:10.1016/s2665-9913(26)00076-7 · Cited by 1
Fu, E., Qin, H., Tian, Z., Dong, H., Zou, D., & Liu, N. (2026) · Forensic Science Medicine and Pathology · doi:10.1007/s12024-026-01224-7 · Cited by 1
“Once the algorithm highlights findings agreed upon by forensic experts, the experts’ atten - tion to other relevant findings overlooked by the algorithm may diminish, thereby introducing a certain degree of bias [107 ].”
When Help Hurts: Verification Load and Fatigue with AI Coding Assistants
Fan, G. F., Liu, D., Pan, L., & Zhang, R. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791176 · Cited by 6
Attitudes, Imagined Roles, and Governance Boundaries for AI in Decentralized Social Media
Zhang, Z., Zhao, J., Wang, G., Viswanathan, S., Ross, T., Johnston, S., Liu, D., Noh, H., Kleek, M. G. V., & Shadbolt, N. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790295 · Cited by 2
“Across fields such as aviation, clinical decision-support, and online platforms, studies consistently document automation bias : people often over-trust or defer to automated outputs even when they are incorrect [22, 42].”
Aqtam, I. F., Shouli, M. M., & Alkorom, S. M. M. (2026) · Journal of Nursing Management · doi:10.1155/jonm/2678581 · Cited by 1
The synthetic transformation of the art world: ethical tensions
Calvo, P. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06984-9 · Cited by 1
“Recent studies have shown that when humans receive some form of assistance from an automated system, such as in performing creative tasks, they tend to consider its responses and recommendations as superior (Skitka et al., 2000; Goddard et al., 2012).”
Reframing the AI alignment problem: insights from business applications
Breeden, J. L. (2026) · AI and Ethics · doi:10.1007/s43681-026-01137-9 · Cited by 1
“[ 25] As AI demonstrates competence, human reviewers increasingly default to approval, transforming substantive review into rubber-stamping.”
A Novel Workflow for Artificial Intelligence-Enhanced Patient Messaging Services
Allen, M. R., Tiyyala, V. M., Ramesh, K. K., Desai, N., Shiach, J., Dredze, M. H., & Ayers, J. W. (2026) · Applied Clinical Informatics · doi:10.1055/a-2852-9026 · Cited by 1
Plausibility, persuasion, and truth: why language models may appear designed to deceive
Vidotto, G. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-07513-4 · Cited by 4
Syailendra, E. A., Arshad, H., Lopez‐Ramirez, F., Tixier, F., Kawamoto, S., Fishman, E. K., & Chu, L. C. H. (2026) · Korean Journal of Radiology · doi:10.3348/kjr.2026.0003 · Cited by 1
“Automation bias can alter human diagnostic behavior by increasing the susceptibility to erroneous AI suggestions (commission errors) or by reducing vigilance when abnormalities are not flagged (omission errors) [86,87].”
Jeong, M.-A., & Kim, S.-D. (2026) · Applied Sciences · doi:10.3390/app16105146 · Cited by 4
“First, the vast majority of published studies evaluating AI-CDSS diagnostic performance use retrospective or observational designs, which cannot establish causal effects on clinician decision-making [30–32 ,35, 36].”
Talking to Ourselves Through a Smart Mirror: Artificial Confidence in Human–AI Interaction
Hochman, G. (2026) · Systems · doi:10.3390/systems14060627 · Cited by 1
Rebelo, P. E. C. (2023) · RCMOS - Revista Científica Multidisciplinar O Saber · doi:10.51473/rcmos.v1i1.2023.2455 · Cited by 1
Artificial Sages: Can a Language Model be an Epistemic Expert?
Gómez, G. M., Torres, A. G., & Garzón‐Vico, A. (2026) · Philosophy of Management · doi:10.1007/s40926-026-00385-3 · Cited by 1
“…on LLMs in different work contexts have been integrated into the study of different biases: on the one hand, automation bias, which describes our tendency to overrely on automated systems, ignoring evidence against their performance and reliability (Skitka et al. 1999 ; Goddard et al. 2012 ).”
The Pseudo-Confidence Paradox: The Epistemic Gap in Everyday AI Use
Kurmanbayeva, L. T., Tanabayeva, A., Doszhanova, A. I., Shashayeva, G. K., Bakarassov, D., & Knar, E. (2026) · Philosophies · doi:10.3390/philosophies11030097 · Cited by 1
Ostrovsky, D. A., Haran, U., & Novack, V. (2026) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2026.107032 · Cited by 1
Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators
Pitts, G., Rani, N., & Mildort, W. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29763-1_28 · Cited by 4
Matschak, T., Nastjuk, I., Trang, S. T.-N., & Benitez, J. (2026) · Decision Support Systems · doi:10.1016/j.dss.2026.114721 · Cited by 1
“Importantly, consultations constitute the behavioral building blocks of decision performance in ADM settings [2,23,27].”
Xu, S. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1849190 · Cited by 1
“Studies on automation bias — the tendency of human operators to over-rely on automated recommendations, particularly under time pressure — have documented this phenomenon across aviation, nuclear operations, and clinical settings (3).”
Decoster, R., Toomey, R. J., Davis, M., & RYAN, M. (2026) · Journal of Medical Radiation Sciences · doi:10.1002/jmrs.70109 · Cited by 1
AI agents as the potential next operational layer in reproductive medicine
Shoham, Z. (2026) · Journal of IVF-Worldwide · doi:10.46989/001c.163012 · Cited by 1
Vinas, A., Blanco, F., & Matute, H. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001490 · Cited by 3
“First, automation bias [24], which drives participants to the default response of believing the AI indication without question.”
Nanda, P. K. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2697344 · Cited by 1
Torres–Martínez, S. (2026) · AI and Ethics · doi:10.1007/s43681-026-01194-0 · Cited by 1
Srivatsa, S. S. (2026) · JVS-Vascular Insights · doi:10.1016/j.jvsvi.2026.100492 · Cited by 1
Medical student reliance on artificial intelligence in nephrology education
Kirpalani, A., & Wang, P. Z. T. (2026) · Journal of Nephrology · doi:10.1093/joneph/aajag033 · Cited by 2
The Dependency Dilemma: How Machine Learning Decision Aids can Undermine Skill Growth
Bauer, K., Nofer, M., Henrich, B., Drachsler, H., & Hinz, O. (2026) · Business & Information Systems Engineering · doi:10.1007/s12599-026-01014-z · Cited by 1
“at the expense of negatively affecting employees’ skill development (see, e.g., Orlikowski 1991; Dzindolet et al. 2002; Goddard et al. 2012; Alavi and Leidner 2001; McCall et al. 2008).”
Akbasli, I. T., Oztürk, B., Serin, O., Doğan, V., Berikol, G. B., Comeau, D. S., Celi, L. A., & Ozguner, O. (2026) · Journal of medical imaging · doi:10.1117/1.jmi.13.4.045501 · Cited by 2
“2,14,15 44 – 47 Because this is a safety-sensitive diagnostic setting, any pathway toward routine clinical use should also be framed within medical-device expectations, including clear intended use, clinical validation, human-factors evaluation, and structured cybersecurity-style threat modeling…”
Berghea, F., Dinca, A. L., Ciuc, D. M., & Dincă, G. V. (2026) · Applied Sciences · doi:10.3390/app16168338 · Cited by 1
“The absence of demographic effects, including those of experience and AI exposure, contrasts with the automation-bias literature, in which lower experience is associated with a greater tendency to change decisions in response to automated advice [14].”
Vernic, C., Petre, I., Păpurică, M., Mada, L., Buriman, R., & Ursoniu, S. (2026) · International Medical Education · doi:10.3390/ime5030084 · Cited by 1
Warner, S. C., Stucky, C. H., Haegerich, T. M., & Yauger, Y. J. (2026) · JMIR Human Factors · doi:10.2196/102683 · Cited by 1
“The result is either uncritical acceptance, which introduces automation bias, or reflexive dismissal, which undermines the tool’s purpose [15].”
Liu, B. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1896492 · Cited by 1
Maturo, F., Micozzi, A., & Porreca, A. (2026) · SN Social Sciences · doi:10.1007/s43545-026-01683-3 · Cited by 1
“…and Oppenheimer 2009 ; Connell and Keane 2004, 2006 ; Lombardi et al. 2016; Reber and Schwarz 1999 ), automation bias and trust in automation (Goddard et al. 2012; Lee and See 2004; Parasuraman and Manzey 2010 ), algorithmic authority (Gillespie 2014 ; Sundar and Nass 2000 ), epistemic…”
Human-AI Collaboration in Radiology: The Blind Spots
Kim, S. H., Adams, L. C., Wiestler, B., & Hedderich, D. M. (2026) · Radiology Artificial Intelligence · doi:10.1148/ryai.260325 · Cited by 1
Tolsgaard, M. G., Grierson, L., Pusic, M. V., & Turner, L. (2026) · Medical Education · doi:10.1111/medu.70309 · Cited by 3
Who Read It First? Documenting Independent Judgment in AI-Assisted Radiology
Henderson, J. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.09.007 · Cited by 1
Nitchals, C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01379-7 · Cited by 1
Batool, S., & Adeel, A. (2026) · Innovation Management Frontiers · doi:10.36923/imf.v1i1.432 · Cited by 1
“…reliance on AI is also associated with automation bias and poorer discrimination between appropriate and inappropriate recommendations, particularly when trust is miscalibrated (Goddard, Roudsari, & Wyatt, 2012; Klingbeil, Grützner, & Schreck, 2024; Ahmad & Mehmood, 2025; Romeo & Conti, 2026).”
Nitchals, C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01414-7 · Cited by 1
“Automation can be misused, disused, or abused; users may rely on incorrect recommendations, neglect non-automated information, or fail to monitor a reliable system closely enough to detect rare errors [ 8, 10, 13, 15 ].”
Morfaki, C. (2026) · Hospitals · doi:10.3390/hospitals3040019 · Cited by 1
Chandio, K. A., Iftikhar, L., Shehzad, A., Rao, I., Waseem, M., Anas, M., Farrukh, M., Shalabi, A. A., Talaat, S., Şen, S., et al. (2026) · The Saudi Dental Journal · doi:10.1007/s44445-026-00249-6 · Cited by 1
“Another important challenge is automation bias, whereby clinicians may over-rely on AI-generated recommendations without critically evaluating the outputs (Goddard et al. 2012 ).”
Chen, J., & Wang, F. (2026) · Oral and Maxillofacial Surgery Clinics of North America · doi:10.1016/j.coms.2026.09.008 · Cited by 1
Annals for Hospitalists Inpatient Notes - Realizing the Promises of Hospital Electronic Order Sets
Li, R., & Chen, J. H. (2020) · Annals of Internal Medicine · doi:10.7326/m20-5164 · Cited by 1
“The resulting information overload from this comprehensive list of options may hinder more than help when managing a complex patient with an evolving condition (2).”
BackgroundGadala, M., Strigini, L., & Ayton, P. (2021) · City Research Online (City University London) · doi:10.48550/arxiv.2106.13544 · Cited by 1
“We hope this work will encourage other researchers in the area of automation bias to recognize adjustable thresholds as a potential solution to some automatedmediated errors, especially since reviews in this area (even recent ones) (Goddard et al., 2011; Lyell & Coiera, 2016) do not mention it.”
BackgroundInfluentialRight to Contest AI Diagnostics
Ploug, T., & Holm, S. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_267-1 · Cited by 4
Lindvall, M. (2021) · Linköping studies in science and technology. Dissertations · doi:10.3384/diss.diva-176117 · Cited by 1
Reinforcement Learning and Affective Computing
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502405 · Cited by 1
Rights and responsibilities of electronic health records (EHR) users caring for children
Sittig, D. F. (2013) · Archivos Argentinos de Pediatria · doi:10.5546/aap.2013.eng.469 · Cited by 1
Pearls and pitfalls in emergency CT neuroangiography through the lens of bias and error
Barkovich, E. J., Batheja, V., Hong, T., Rao, J., & Javan, R. (2023) · Emergency Radiology · doi:10.1007/s10140-023-02143-8 · Cited by 1
Mitigating Bias in Algorithmic Decision-making: Evaluating Claims and Practices
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603212 · Cited by 1
Artificial intelligence in pediatrics
Knake, L. A., Rogerson, C. M., Winter, M. C., & Kandaswamy, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00017-6 · Cited by 1
Empowering Healthcare Professionals Through AI-Powered Lifelong Learning for Improving Patient Care
Rajendran, R., Subramanian, Y. R., Poddar, S., & Geetha, V. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2440-0.ch006 · Cited by 1
The Groundwork for an Ethics of Artificial Intelligence in Defence
Taddeo, M. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780197745441.003.0001 · Cited by 1
Inspo: Writing with Crowds Alongside AI
Huang, C.-Y., Gautam, S., Brooks, S. M., Lin, Y.-F., Knearem, T., & Huang, T.-H. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720193 · Cited by 10
Schlögl-Flierl, K., & Ziethmann, P. (2025) · Magdeburger Forschungen zu Bildungs-, Kultur- und Sozialwissenschaften · doi:10.1007/978-3-658-46631-2_3 · Cited by 2
Krüger, M., Walker, B. N., & Chuang, L. L. (2019) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/3349263.3350763 · Cited by 1
“Also commonly reported automation issues known as automation bias [15, 11], i.”
BackgroundWeighted Utility: A Utility Metric Based on the Case-Wise Raters’ Perceptions
Campagner, A., Conte, E., & Cabitza, F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84060-0_13 · Cited by 1
The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603204 · Cited by 1
Boloré, S., & Garcia-Loiseau, J. (2022) · Research and theory for nursing practice · doi:10.1891/rtnp-2021-0093 · Cited by 1
Kiess, C. (2013) · IUScholarWorks (Indiana University) · doi:10.7912/c2/927 · Cited by 1
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning SystemsPreprint
Iuzzolino, M. L., Umada, T., Ahmed, N. R., & Szafir, D. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00762 · Cited by 1
“Importantly, these errors have been demonstrated to reverse an analyst’s correct assessment and are representative of automation bias and complacency and result from either an attentional bias or insufficient attention and monitoring of automation system output [17].”
BackgroundWhen predictive analytics goes wrong: what can healthcare learn from Formula 1?
Shah, S. F. H., & Sheridan, Z. (2020) · British Journal of Hospital Medicine · doi:10.12968/hmed.2020.0389 · Cited by 1
Petersen, E., Potdevin, Y., Mohammadi, E., Zidowitz, S., Breyer, S., Nowotka, D., Henn, S., Pechmann, L., Leucker, M., Rostalski, P., et al. (2021) · arXiv (Cornell University) · Cited by 1
Prével, A., & Bue, S. L. (2022) · Preprints.org · doi:10.20944/preprints202203.0070.v1 · Cited by 1
Cognitive bias in diagnostic radiology
Murphy, A., Dixon, A., & Deng, F. M. (2018) · Radiopaedia.org · doi:10.53347/rid-59601 · Cited by 1
Autonomy-Respectful E-Coaching Systems
Kamphorst, B. A. (2020) · Utrecht University Repository (Utrecht University) · doi:10.33540/344 · Cited by 9
Schreiber, R., & McGreevey, J. D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91200-6.00009-7 · Cited by 1
Regulação da inteligência artificial na saúde
Dourado, D. D. A. (2024) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2024.tde-23042024-111255 · Cited by 2
The workload-performance relationship under human supervision of algorithmic systemsPreprint
Men, C., den Broeke, M. M. V., Verschelde, M., & Roets, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4891595 · Cited by 1
Lane, J. N., Boussioux, L., Ayoubi, C., Chen, Y. H., Lin, C., Spens, R., Wagh, P., & Wang, P.-H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4914367 · Cited by 2
Auditor Models to Suppress Poor AI Predictions Can Improve Human-AI Collaborative PerformancePreprint
Brown, K. E., Wrenn, J. O., Jackson, N. J., Cauley, M. R., Collins, B. X., Novak, L. L., Malin, B., & Ancker, J. S. (2025) · medRxiv · doi:10.1101/2025.06.24.25330212 · Cited by 1
“Over-reliance on decision-support technology such that human performance is harmed has been studied previously in AI and non-AI tools (14,15).”
Cleverly, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5359658 · Cited by 1
Mansoor, M. A. (2025) · medRxiv · doi:10.1101/2025.08.05.25333046 · Cited by 2
Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The PublicPreprint
Xu, X., Hu, H., Zhang, H., Wang, W. K., Wang, R., Soenksen, L. R., Badri, O., Jafry, S., Burger, E., Nwandu, L., et al. (2025) · medRxiv · doi:10.64898/2025.12.19.25342205 · Cited by 1
Mai, M. V., Ozkaynak, M., Marquard, J. L., Simonson, R. J., Holden, R. J., Barton, H. J., Catchpole, K. C., Jamil, M., Brauer, S., & Kandaswamy, S. (2026) · medRxiv · doi:10.64898/2026.01.18.26344350 · Cited by 1
The prediction paradox: Artificial intelligence, genetics, and occult metastasis in thyroid cancer
Toro-Tobón, D. (2026) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgag065 · Cited by 1
Ambient artificial intelligence scribes in dentistry
Zhou, K. X., Ng, Q. X., Wu, D. T., & Gigliotti, J. (2026) · The Journal of the American Dental Association · doi:10.1016/j.adaj.2026.01.016 · Cited by 1
Naye, F., Ouellet, S., Yousefi, F., Cachinho, C., Bergeron, F., Bhattacharyya, O. K., Stamenova, V., & Sasseville, M. (2026) · doi:10.2196/preprints.96882 · Cited by 1
Boloré, S. (2022) · theses.fr (ABES) · doi:10.70675/bed89993z59dbz48bczbb1dzc92eb81c2177 · Cited by 1
Tajima, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6632719 · Cited by 1
Clinical safety evaluation of ambient voice technologies in surgical documentationPreprint
O’Kane, R., Stonehouse‐Smith, D., Shirazi, M. R. A., Hutchinson, R., Seehra, J., Papageorgiou, S. N., & Cobourne, M. T. (2026) · Research Square · doi:10.21203/rs.3.rs-9654603/v1 · Cited by 1
From adoption to architecture: Agentic AI in surgical data science
Kuo, P. C. (2026) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2026.117136 · Cited by 1
Mohiyeddini, C. (2026) · doi:10.2196/preprints.107161 · Cited by 1
Jung, W., Jeon, K., & Babon-Ayeng, P. (2026) · Proceedings of the 2026 ACM Sustainability Week · doi:10.1145/3765611.3815499
“Automation bias has been shown to persist even among domain experts [8], and the relationship between domain knowledge and AI reliance may be non-linear rather than simply protective [23].”
Artificial intelligence in nursing education: Balancing innovation and human-centered care
Nguyen, R. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107300
Iorio, L. (2026) · Athens Journal of Law · doi:10.30958/ajl.12-2-3
L'acte administratif algorithmique
Stepanov, A. (2026) · doi:10.70675/adc05be3za62ez4b4fz81e7zabf744e60dbf
When Recruiters Meet Algorithms: Advice Taking and Bias in AI‐Assisted Recruitment
Lacroux, A., & Martin Lacroux, C. (2026) · International Journal of Selection and Assessment · doi:10.1111/ijsa.70085
Limits of Interventions in AI-Assisted Decision Making
Shrestha, S., Ahn, E., & Vorobeychik, Y. (2026) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v9i4.43309
“…AI operates at the level of recommendation rather than direct action, the mere presence of an algorithmic suggestion can systematically shift human judgment toward the algorithm’s conclusion, a phenomenon known as automation bias (Goddard, Roudsari, and Wyatt 2012; Alon-Barkat and Busuioc 2023).”
Tao, Y., Meyer, J., & Kizilcec, R. F. (2026) · arXiv · doi:10.48550/arxiv.2609.23936
“This tendency is consistent with automation bias, in which people overrely on automated recommendations and accept them without sufficient independent verification (Goddard et al., 2012).”
From Oracles to Productive Friction: Designing for Meaningful Human Oversight in Clinical AI
Rubegni, E., Natali, C., Valsecchi, C., Rizzo, S., Ayoub, O., & Facchini, A. (2026) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v9i3.43300
Clinical decision support systems in the care of hospitalised patients with diabetes
Nirantharakumar, K. (2013) · University of Birmingham Institutional Research Archive (University of Birmingham)
Medición de la función pulmonar en preescolares: del laboratorio a la práctica clínica
Aguerre, V. (2013) · Archivos Argentinos de Pediatria · doi:10.5546/aap.2013.468
Salient Human Factors Issues in Motor Vehicle Accidents
Fatolitis, P. G. (2019) · Journal of Ergonomics · doi:10.35248/2165-7556.19.9.e184
Objective and Subjective Responsibility of a Control-Room Worker.Preprint
Douer, N., Redlich, M., & Meyer, J. (2019) · arXiv (Cornell University)
Making Automation Explicable: A Challenge for Philosophy of Technology
Smith, D. (2019) · New Formations · doi:10.3898/newf:98.05.2019
Hoe artificiële intelligentie, digitalisering en big data ons kunnen helpen bij verantwoordbare zorg
BIESEN, W. V., Veys, N., Decruyenaere, J. M. A., Peleman, R. A., & Sterckx, S. (2021) · Tijdschrift voor Geneeskunde · doi:10.47671/tvg.77.20.110
Government Models, Decision-Making, and the Public Law Presumption of DisclosurePreprint
Maxwell, J., & Tomlinson, J. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3900833
Metadata
- Authors
- Goddard, K., Roudsari, A., & Wyatt, J. C.
- Affiliations
- City, University of London (GB)
- University of Warwick (GB)
- Corresponding author
- Goddard, Kate
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-01
- Volume
- 19
- Issue
- 1
- Pages
- 121-127
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC3240751
- OpenAlex
- W2281090488
- Cited by
- 1,162
- References
- 77
- Citations by year
- 2026: 513 · 2025: 180 · 2024: 111 · 2023: 88 · 2022: 78 · 2021: 61 · 2020: 39 · 2019: 29 · 2018: 23 · 2017: 8 · 2016: 7 · 2015: 4 · 2014: 6 · 2013: 11 · 2012: 4
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- automation bias, clinical decision support systems, human-automation interaction, over-reliance on automation, trust in automation, cognitive style, workload, task complexity, time constraints, user accountability, DSS design, clinical decision-making
- MeSH terms
- Attitude to Computers, Automation, Aviation, Humans, Task Performance and Analysis, Decision Support Systems, Clinical
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
