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 501–600 of 1,008
The Role of Trust and Usability to Enable Spaceflight Crew Autonomy
Márquez, J. J., Sullivan, D., & Karasinski, J. A. (2022) · AIAA Accelerating Space Commerce, Exploration, and New Discovery Conference (ASCEND) · doi:10.2514/6.2022-4262 · Cited by 3
Ein kritischer Blick auf die Idee eines Patient Preference ‚Predictors‘
Hiekel, S. (2024) · Zeitschrift für Ethik und Moralphilosophie · doi:10.1007/s42048-024-00188-z · Cited by 3
Ethik der künstlichen Intelligenz in der Medizin
Giovanni Maio, M. A. (2024) · DMW - Deutsche Medizinische Wochenschrift · doi:10.1055/a-2302-8919 · Cited by 3
“Selbst wenn menschliche Entscheider den Vorschlag des Algorithmus lediglich als eine Informationsquelle heranziehen, werden sie doch stark davon beeinflusst und neigen dazu, Vorge-schlagenes zu bestätigen [10].”
BackgroundAlgorithmic Decision Making in Education: Challenges and Opportunities
Levantis, N., & Sgora, A. (2024) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon60312.2024.10578645 · Cited by 6
“Complex tasks and time constraints can lead to an excessive reliance on decision-making tools and their output [24], [25], while educators’ or students’ experience and confidence levels may also influence their behavior [26], [27].”
BackgroundApplications and potential of machine, learning augmented chest X-ray interpretation in cardiology
Milne, M., Ahmad, H. K., Buchlak, Q. D., Esmaili, N., Tang, C. H. M., Seah, J. C. Y., Ektas, N., Brotchie, P., Marwick, T. H., & Jones, C. M. (2024) · Minerva Cardiology and Angiology · doi:10.23736/s2724-5683.24.06288-4 · Cited by 2
Kara, M. A. (2024) · Medicine Health Care and Philosophy · doi:10.1007/s11019-024-10241-8 · Cited by 3
“An increase in the user’s accountability for the consequences of decisions may also have a mitigat - ing effect (Goddard et al. 2012 ; Parasuraman and Manzey 2010).”
InfluentialArtificial Intelligence and Financial Services
Langenbucher, K. C. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009367783.020 · Cited by 3
Exploring and Steering the Moral Compass of Large Language Models
Tlaie, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88223-4_30 · Cited by 8
Evaluating the Performance of Large Language Models on Multispecialty FRCS Section 1 Questions
Ahmed, H. S. (2025) · Journal of Surgical Research · doi:10.1016/j.jss.2025.06.026 · Cited by 2
Lang, B. H. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00990-6 · Cited by 3
“Studies in automation bias have demonstrated widespread susceptibility to grant - ing unwarranted epistemic authority to machines, and this may apply even when the field of inquiry encompasses one’s own values and beliefs (Goddard et al., 2012).”
Medication management: Reducing drug errors, striving for safer practice
Edwards, S. L., & Axe, S. (2018) · Nurse Prescribing · doi:10.12968/npre.2018.16.8.380 · Cited by 3
KI-gestützte Selbstvermessung der Psyche: Philosophisch-ethische Implikationen
Friedrich, O., Seifert, J., & Schleidgen, S. (2021) · Psychiatrische Praxis · doi:10.1055/a-1364-5068 · Cited by 7
“Ein automation bias, der die Beobachtung in der Psychologie beschreibt, dass Menschen dazu neigen, Entscheidungen zu bevorzugen, die von einem automatisierten System getroffen werden [29], könnte eine solche vermeintlich fehlerhafte, d.”
BackgroundTrusted Partners: Human-Machine Teaming and the Future of Military AI
Konaev, M., Huang, T. C., & Chahal, H. (2021) · doi:10.51593/20200024 · Cited by 4
Bias Mitigation in Data SetsPreprint
Brown, S., Carrier, R., Hickok, M., & Smith, A. L. (2021) · doi:10.31235/osf.io/z8qrb · Cited by 3
Souders, D. J., Agrawal, S., & Peeta, S. (2022) · doi:10.5703/1288284317573 · Cited by 3
Leslie, S. E., Durden, J. A., & Tevis, S. E. A. (2024) · Cancer · doi:10.1002/cncr.35651 · Cited by 3
“10,12 This includes educating patients on how to discern credible sources from unreliable ones and emphasizing the importance of using these tools to supplement, rather than replace, discussions with their health care team.”
Rethinking Human-AI Collaboration in Radiology
Shirangi, A., Pittinsky, T. L., & Gichoya, J. W. (2026) · Radiology · doi:10.1148/radiol.252760 · Cited by 3
AI in pharmacy practice: Devolutionary or evolutionary?
Rosenthal, M., Austin, Z., Hoti, K., & Tsuyuki, R. T. (2026) · Canadian Pharmacists Journal / Revue des Pharmaciens du Canada · doi:10.3138/cpj-26-0226 · Cited by 3
Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education
Quinn, T. P., & Coghlan, S. (2021) · arXiv · doi:10.48550/arxiv.2109.02866 · Cited by 13
Gefahren für die Autonomie durch gesundheitsbezogenes Self-Tracking
Hiekel, S. (2024) · Ethik in der Medizin · doi:10.1007/s00481-024-00832-7 · Cited by 2
“Und das kann zunehmen, je komplexer die Aufgabe ist, je mehr Arbeitsbelastung besteht und wenn die Person, die eine Entscheidung zu treffen hat, wenig Erfahrung auf dem Gebiet hat, in dem Einschätzungen gemacht oder Entscheidungen getroffen werden sollen (Goddard et al. 2012).”
Chapa, J. O. (2024) · Journal of Military Ethics · doi:10.1080/15027570.2024.2439654 · Cited by 2
“For automation bias, see Goddard, Roudsari, and Wyatt (2012) and Wickens et al. (2015).”
Postill, G., Dent, A., Dombroski, J. D., Verma, A. A., Myers, J., & Apramian, T. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000917 · Cited by 2
“Evaluation of their perspectives can also identify, and potentially address, concerns for automation bias [14–16].”
Is Consent-GPT valid? Public attitudes to generative AI use in surgical consent
Allen, J. W., Hannikainen, I. R., Savulescu, J., Wilkinson, D. J. C., & Earp, B. D. (2025) · AI & Society · doi:10.1007/s00146-025-02644-9 · Cited by 4
“This phenomenon manifests particularly in fast-paced healthcare environments where cognitive resources are strained (Ho and Vuong 2025; Goddard et al. 2012).”
The Role of Artificial Intelligence in Workforce Adaptation and Skill Development
Karmali, Y. (2026) · Biomedical visualization · doi:10.1007/978-3-032-06520-9_10 · Cited by 2
Brown, K. E., Wrenn, J. O., Jackson, N. J., Cauley, M. R., Collins, B. X., Novak, L. L., Malin, B., & Ancker, J. S. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf235 · Cited by 2
“14,15 However, this over-reliance may become more impactful as the scope and power of ML continues to grow.”
Zhang, L., & Li, Q. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1739478 · Cited by 4
Ofem, U. J., Ajuluchukwu, E. N., Undie, S. B., Orim, F. S., Olom, P. A., Adie, J. A., Ukatu, J. O., & Ebaye, A. S. (2026) · Discover Education · doi:10.1007/s44217-026-01184-8 · Cited by 5
Algorithmic ethics in corporate contexts: Knowledge mapping for responsible management
Barreno-Alcalde, S., Delso-Vicente, A.-T., & Rivera-Heredia, A. (2026) · Journal of Management & Organization · doi:10.1017/jmo.2026.10089 · Cited by 4
“Excessive reliance on automated systems (the automation bias) leads to uncritical acceptance of algorithmic decisions (Goddard, Roudsari & Wyatt, 2012), while limited understanding of personal data processing restricts user agency (Kozodoi et al., 2022; Selbst & Powles, 2017).”
Kolesnyk, S., Souza, J. D. P., Bearth, A., Aicher, L., Wilks, M., Mosig, A., & Fritsche, E. (2026) · Toxicology · doi:10.1016/j.tox.2026.154439 · Cited by 2
Patient and Clinician Attitudes Toward Mobile Health Apps: Qualitative Study
James, C. A., Krein, S. L., Yon, S., Dorsch, M. P., Piette, J. D., & Nallamothu, B. K. (2026) · JMIR mhealth and uhealth · doi:10.2196/77519 · Cited by 2
“This concern is well-founded as the introduction of technology can lead to a form of automation bias in which clinicians over-rely on mHealth apps and their outputs, or inappropriate delegation of tasks to mHealth apps [64].”
A nursing perspective on human-AI collaboration in personalized breast cancer care pathways
Zhang, J., Zhao, X., Tao, J.-H., & Su, D.-C. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1784401 · Cited by 2
“To minimize ambiguity and reduce automation bias, we propose a streamlined governance structure applicable across all stages of care (58, 59).”
Tang, H., Li, L., & Chen, L. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2621990 · Cited by 2
“This perception leads to a positive automation bias , a form of blind acceptance in which people overly trust information simply because it is machine-or algorithm-generated (Goddard et al., 2012).”
Matsui, M. (2026) · Medicine Health Care and Philosophy · doi:10.1007/s11019-026-10346-2 · Cited by 2
“…by empirical research on clinical decision-support systems, which shows that prescriptive or recommendation-framed outputs are more likely to be accepted uncritically than descriptive findings—a phe - nomenon widely discussed as automation bias (Goddard et al. 2012; Abdelwanis et al. 2024 ).”
Abady, E. M. A., Elewa, M., Elrefaey, H. A., Mathew, K. T., Tamvakologos, P. I., Kuhn, K., & Alsabri, M. (2026) · Sage Open Pediatrics · doi:10.1177/30502225261445743 · Cited by 2
“24 Parallel integration systems, where AI operates alongside rather than preceding clinical workflow, have demonstrated particular success.”
Clinical agents fail silently on patient identity
Klang, E., Glicksberg, B. S., Gorenshtein, A., Gavin, N., Freeman, R., Stump, L. S., Charney, A. W., Ting, D. S. W., Omar, M., & Nadkarni, G. N. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106514 · Cited by 7
Teaching artificial intelligence in the emergency department: A practical guide for educators
Heslin, S. M. (2026) · Artificial Intelligence in Emergency Medicine · doi:10.1016/j.aiemed.2026.100026 · Cited by 2
Wu, S. (2026) · Journalism Practice · doi:10.1080/17512786.2026.2682272 · Cited by 2
Gu, C., Zhuo, X., & Jiang, K. (2026) · Journal of Health Communication · doi:10.1080/10810730.2026.2681655 · Cited by 2
“In high-risk domains such as health, this mechanism reflects automation bias, where reliance on AI reduces critical evaluation (Goddard et al., 2012).”
Trust in AI vs human expert: investigating behavioral intention among employees
Yuan, Y., Hao, F., Qiu, X., & Kong, H. (2026) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-07-2025-1078 · Cited by 3
AI, Evidentiary Authority, and the Right to a Fair Trial in Criminal Proceedings
Kocagul, H., & Kartal, M. (2026) · Laws · doi:10.3390/laws15030058 · Cited by 4
Du, P., Tingting, L., & Xian, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1889402 · Cited by 2
Positive psychology 4.0: a science of consequence for a world that can no longer wait
van Zyl, L. E. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1726065 · Cited by 2
“However, these benefits only materialize when humans remain actively engaged in the decision-making process rather than passively deferring to algorithmic recommendations (Goddard et al., 2012).”
Alibašić, H. (2026) · Systems · doi:10.3390/systems14070879 · Cited by 2
Alqheedan, A., & Alzughaibi, S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1882814 · Cited by 2
Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people
Xu, X., Hu, H., Zhang, H., Wang, W. K., Wang, R., Soenksen, L. R., Badri, O., Jafry, S., Burger, E., Nwandu, L., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04553-w · Cited by 8
AI agents in clinical medicine: delegation, supervision, and the problem of correlated failure
Waisberg, E., & Guarnieri, J. W. (2026) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-026-04584-9 · Cited by 2
Designing for Human-Centred Decision Support Systems in PHM
McDonnell, D., Balfe, N., Al‐Dahidi, S., & O’Donnell, G. E. (2014) · PHM Society European Conference · doi:10.36001/phme.2014.v2i1.1558 · Cited by 2
Evaluation of Biomedical and Health Information Resources
Friedman, C. P., & Wyatt, J. C. (2021) · Springer eBooks · doi:10.1007/978-3-030-58721-5_13 · Cited by 3
Self-citationWalking the line: Mindfulness with IT in hospital medication routines
Lichtner, V., Karanasios, S., & Iannacci, F. (2023) · Information and Organization · doi:10.1016/j.infoandorg.2023.100475 · Cited by 2
Confidence Sequences for Evaluating One-Phase Technology-Assisted Review
Lewis, D. D., Gray, L., & Noel, M. (2023) · International Conference on Artificial Intelligence and Law · doi:10.1145/3594536.3595167 · Cited by 2
“2United States Trademark 86/634255 3United States Trademark 86/634265 recommendation) occur [18].”
BackgroundShukla, S., & Shukla, S. K. (2023) · Financial Markets Institutions and Risks · doi:10.61093/fmir.7(3).1-11.2023 · Cited by 6
Johnston, C. M., Vossler, P., Blessenohl, S., & Vayanos, P. (2023) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3617694.3623254 · Cited by 2
“Any recommendation system faces the potential issue of automation bias, where individuals are more likely to trust in decisions proposed by AI models as opposed to humans, see e.g., [16].”
BackgroundEffects of Presenting Multiple Types of AI Explanations for Visual Task
Maehigashi, A., Fukuchi, Y., & Yamada, S. (2024) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3687272.3688328 · Cited by 2
“In addition, regardless of the amount of AI explanation, people often follow AI results without adequate verification due to high initial trust in AI, a phenomenon known as automation bias [10].”
McLaughlin, L., McFadden, S. L., Villikudathil, A. T., McConnell, J., Hughes, C., Bond, R., & Rainey, C. (2024) · Journal of Medical Radiation Sciences · doi:10.1002/jmrs.850 · Cited by 2
VisTaxa: Developing a Taxonomy of Historical Visualizations
Zhang, Y., Chen, X., Zheng, W., Guo, Y., Li, G., Chen, S., & Yuan, X. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3567132 · Cited by 4
Alrawashdeh, A., Ihtoub, S., Alkhatib, Z. I., Alwidyan, M., Khader, Y. S., Rawashdeh, S., Alqahtani, S., Stub, D. A., Alhamouri, R., Alkhazali, I. E., et al. (2025) · Archives of Academic Emergency Medicine · doi:10.22037/aaemj.v13i1.2627 · Cited by 2
“…test may have been influenced by the knowledge of other tests since ambulance clinicians or a specialist who received transmitted 12-lead ECGs may have been influenced by the printed diagnosis suggested by the computer software (20, 26, 35, 37, 39, 41, 45), which may introduce automation bias (54).”
Human-at-the-Helm: Redefining the role of artificial intelligence in student support
Rhodes, J. E. (2026) · Applied Developmental Science · doi:10.1080/10888691.2026.2683481 · Cited by 2
An Overview of HIT-Related Errors
Magrabi, F., Ong, M., & Coiera, E. W. (2016) · Springer eBooks · doi:10.1007/978-3-319-31123-4_2 · Cited by 2
Mathematical Model for Computer-Assisted Modification of Medication Dosing Rules
Grabel, M., Vaughan, B. L., Dexheimer, J. W., & Kirkendall, E. S. (2019) · Biomedical Informatics Insights · doi:10.1177/1178222619829079 · Cited by 2
“22,23 This tool has the capability to rapidly assess all drug rules in a corpus in an automated fashion, identify and prioritize candidate rules for editing based on the potential decrease in alert burden or other factors, and pre-sent these rules to a human expert for validation and implementation.”
MethodOperator Responsibility for Outcomes: A Demonstration of the ResQu Model
Douer, N., Redlich, M., & Meyer, J. (2020) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181320641065 · Cited by 2
Human-Machine Hybrid Decision Making with Applications in Auditing
Hooshangi, S., & Sibdari, S. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.026 · Cited by 2
Dhesi, A. S., Wyatt, J. C., Estcourt, L. J., McSporran, W., & Allard, S. (2022) · Transfusion Medicine · doi:10.1111/tme.12872 · Cited by 2
Self-citationAlgorithmic Political Bias Can Reduce Political Polarization
Peters, U. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00576-6 · Cited by 2
“…allocate especially high epistemic authority to algorithms and display ‘automation bias’, i.e. they prefer suggestions from automated decision-making systems and tend to disregard contradictory information made without automation even when it is accurate (Goddard et al., 2012; Logg et al., 2019).”
BackgroundAI - Limits and Prospects of Artificial Intelligence
Klimczak, P., & Petersen, C. (2023) · AI critique · doi:10.1515/9783839457320 · Cited by 7
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603198 · Cited by 2
8 Künstliche Intelligenz in der Medizin
Dössel, O., Schäffter, T., & Rutert, B. (2020) · Medizinisch Wissenschaftliche Verlagsgesellschaft eBooks · doi:10.32745/9783954665389-125 · Cited by 2
Algorithmic Monoculture and Social Welfare
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603211 · Cited by 2
Automation bias and prescribing decision support – rates, mediators and mitigators
Goddard, K. S. (2012) · City Research Online (City University London) · Cited by 2
Self-citationExplainable question answering beyond F1: metrics, models and human evaluation
Schuff, H. (2020) · OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · doi:10.18419/opus-11224 · Cited by 2
Decision-makers Processing of AI Algorithmic Advice: Automation Bias versus Selective Adherence.Preprint
Alon‐Barkat, S., & Busuioc, M. (2021) · arXiv (Cornell University) · Cited by 2
The Effects of Discontinuing Machine Learning Decision SupportPreprint
Bauer, K., Nofer, M., Abdel-Karim, B. M., & Hinz, O. F. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4299664 · Cited by 2
Prével, A., Salatino, A., & Bue, S. L. (2023) · Preprints.org · doi:10.20944/preprints202203.0070.v2 · Cited by 1
Action vs. Attention Signals for Human-AI Collaboration: Evidence from ChessPreprint
Poulidis, S., Ge, H., Bastani, H., & Bastani, O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5128584 · Cited by 2
Embedding Ethics into Medical AI
Meier, L. J. (2025) · doi:10.1002/9781394238651.ch17 · Cited by 2
Clinical Agents Don’t CarePreprint
Klang, E., Glicksberg, B. S., Gorenshtein, A., Gavin, N. P., Freeman, R., Stump, L. S., Charney, A. W., Ting, D. S. W., Omar, M., & Nadkarni, G. N. (2025) · medRxiv · doi:10.1101/2025.10.17.25338226 · Cited by 2
“Humans often over-trust automated outputs, and agents may replicate or even amplify this bias-confidently writing through inconsistencies without hesitation or correction (9,10).”
The Human-AI Contracting ParadoxPreprint
Bastani, H., & Cachon, G. P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5962739 · Cited by 2
Criticmate: Stagewise Human–AI Co-Critique in Single-Screen UI Evaluation
Ko, J., Choi, J., Morales, C., Kim, D., & Ko, M. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790929 · Cited by 1
“For humans, post-hoc interaction with such outputs is opaque and coarse-grained: experts must reverse-engineer the model’s reasoning to correct errors, while novices risk automation bias and over-reliance on machine-generated authority [32, 44, 87].”
Introduction to Applied Affective Computing
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502400 · Cited by 1
Emotion Recognition in the Wild
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502404 · Cited by 2
The potential impact of artificial intelligence in equine practice
Graham, C. (2023) · UK-Vet Equine · doi:10.12968/ukve.2023.7.5.158 · Cited by 1
Just War Theory and the Permissibility of Autonomous Weapons Systems
Taddeo, M. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780197745441.003.0008 · Cited by 1
Artificial Intelligence’s (AI’s) Responsible Use: How to Manage Digital Ethicswashing
Bernini, F., Ferretti, P., & Rosa, F. L. (2024) · SIDREA series in accounting and business administration · doi:10.1007/978-3-031-74292-7_2 · Cited by 1
Chandra, J., Kaur, R., & Sahay, R. (2025) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt64827.2025.10968102 · Cited by 6
Yanagita, S. (2025) · Journal of Mathematical Economics · doi:10.1016/j.jmateco.2025.103131 · Cited by 1
Chen, Y., Tan, X., & Li, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92692-1_14 · Cited by 1
Palmer, A., & Schwan, D. (2025) · The International Library of Bioethics · doi:10.1007/978-3-031-94690-5_4 · Cited by 1
Designing for Artificial Intelligence in High-Risk Domains: A Case Study
Arora, T. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251370724 · Cited by 1
“In regulatory compliance contexts, if an AI system demonstrates high but imperfect reliability in identifying regulatory changes, human operators may reduce their vigilance and fail to detect critical errors or omissions (Goddard et al., 2012).”
Khalifa, A., Abdulaziz, M., Parvez, S. S., Salem, A. M., Panhwer, H. S., Saleem, U., Eldeeb, M., Weld-Ali, T., & Mosaad, M. (2025) · Cureus · doi:10.7759/cureus.95059 · Cited by 3
“Additionally, ensuring interoperability (AI working with different ECG machines, different imaging scanners, and EHR systems) is key to seamless integration [61-64].”
Tamminen, L., Högberg, N., Berggren, K., von Walter, L. W., & Antillón, G. O. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1704112 · Cited by 1
“L-MT’s salary was partly funded by Swedish Research Council Formas, grant number 2022-02779.”
Ben-Shahar, D., Carmeli, A., Sulganik, E., & Weiss, D. (2025) · AI & Society · doi:10.1007/s00146-025-02773-1 · Cited by 4
“Specifically, whereas concepts like automation bias (Goddard et al. 2012; Mosier and Skitka 1996; Skitka 1999) and groupthink (Janis 1982) are important and relevant, they do not fully account for the unique dynamics of AI’s influence in high-stakes group decision-making.”
InfluentialSmall, W. R., Crowley, R. J., Pariente, C. S., Zhang, J., Eaton, K. P., Jiang, L. Y., Oermann, E. K., & Aphinyanaphongs, Y. (2026) · npj Health Systems · doi:10.1038/s44401-025-00059-8 · Cited by 2
“We will also be vigilant for unintended consequences related to hallucinations 39,40 , model biases 41,42 , or automation bias 43 – 45 by end-users that lead to negative patient or operational outcomes.”
Ganie, I. S., Ganie, N. S., Gibson, J. G., & Raniga, S. B. (2026) · Clinical Radiology · doi:10.1016/j.crad.2026.107254 · Cited by 2
Qin, H., Fan, Z., Yang, X., & Ma, B. (2026) · International Journal of Surgery · doi:10.1097/js9.0000000000004724 · Cited by 1
“The positioning of AI as a collaborative “copilot,” however, immediately confronts a fundamental dilemma rooted in the “black box” nature of its decision making [2] .”
Augmenting military decision making with artificial intelligence
Vold, K. (2026) · Law and Governance · doi:10.1017/cfl.2025.10022 · Cited by 3
David-Olawade, A. C., Oguntola, B. O., Olawade, D. B., Bello, O. J., Olawuyi, O. F., & Wada, O. Z. (2026) · Intelligent Hospital · doi:10.1016/j.inhs.2026.100050 · Cited by 1
Zhu, Y., Chen, R., & Zheng, L. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2628148 · Cited by 1
“This can lead individuals to favor algorithmic over human judgment, a phenomenon known as “algorithm appreciation” (Logg et al., 2019), and may even result in over-reliance (Goddard et al., 2012; Mosier et al., 1996).”
Guler, T. M., Ros, P. R., & Ertürk, Ş. M. (2026) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2026.02.020 · Cited by 1
Rizal, P. (2025) · Annals of Innovation in Medicine · doi:10.59652/hrnhkr35 · Cited by 1
“(12) In prescribing contexts, incorrect CDSS can induce omission and commission errors, demonstrating how users can accept false alerts and miss true problems when trust is mis-calibrated.”
Harris, A. R., McGivern, P., Wedgwood, K. C. A., & Gilbert, F. (2026) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2026.1767365 · Cited by 1
“Conversely, clinicians may feel financial, social or time pressures to transfer decision making over to AI-NO systems (Goddard et al., 2012).”
Developing Critical Judgment of Artificial Intelligence in Medical Education: Applied Insights
Heslin, S. M. (2026) · MedEdPublish · doi:10.12688/mep.21479.1 · Cited by 2
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
