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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.
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“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.
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“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.
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“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.
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“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.
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“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
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“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
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“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.
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“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
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“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
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“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
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“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
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“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
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“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
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“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]
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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)
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“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
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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
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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
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“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
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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
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“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
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“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]
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Auto-METRICS: LLM-assisted scientific quality control for radiomics research
de Almeida, J. G., & Papanikolaou, N. K. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112358 · Cited by 8
Influence of automated façades on comfort and energy: A critical review
de la Barra, P., Brembilla, E., Prieto, A., Zaldívar, C. V., Knaack, U., & Luna-Navarro, A. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116290 · Cited by 10
The balance between artificial and human intelligence in clinical practice
Marrella, D., Anttila, T., Ryhänen, J., Miller, R., Liu, B., & Liverneaux, P. A. (2026) · Journal of Hand Surgery (European Volume) · doi:10.1177/17531934251401382 · Cited by 7
“Quantitatively, automation bias increases human error risk by 26% when AI is wrong (Goddard et al., 2012) and unjustly alters 7% of correct assessments under the influence of erroneous AI advice (Rosbach et al., 2025).”
Artificial intelligence in emergency medicine: a narrative review
Rego, A., Arango-Ibañez, J. P., Taylor, R. A., Smith, M. E., Jones, D. D., Pelletier, J., Colletti, J. E., Gottlieb, M. A., & Long, B. (2026) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2026.01.028 · Cited by 7
The comfort of automation: why cognitive sovereignty matters in AI-driven life sciences
Branda, F., & Ciccozzi, M. (2026) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2026.100158 · Cited by 8
Building Transparent and Personalized AI Support in Air Traffic Control
Westin, C., Hilburn, B., Borst, C., Kampen, E.-J. V., & Bång, M. (2020) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc50938.2020.9256708 · Cited by 7
“Several human factors constructs have explored the breakdown in human understanding of automation, including automation surprise [9] [10], out-of-the-loop [11], and loss of situation awareness [12].”
BackgroundReader Perceptions and Impact of AI on CT Assessment of Air Trapping
Retson, T., Hasenstab, K. A., Kligerman, S. J., Jacobs, K. E., Yen, A. C., Brouha, S. S., Hahn, L. D., & Hsiao, A. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021210160 · Cited by 7
“In addition to several examples from the mammography literature (22,23), Liu et al found that a lung nodule detection algorithm performed similarly to support, ranging from aviation to health care, and has been shown to have a greater impact on less experienced physicians (29,30).”
MethodABCs: Differentiating Algorithmic Bias, Automation Bias, and Automation Complacency
Potasznik, A. (2023) · IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS) · doi:10.1109/ethics57328.2023.10155094 · Cited by 9
“Some researchers have posited that automation bias and complacency are so alike as to be considered synonymous, and that “similar outcomes… mean that one effect may be conflated by or confused with another” [16].”
BackgroundShould I trust this model? Explainability and the black box of artificial intelligence in medicine
Petch, J., Bortesi, J. P. T., Nelson, W., Di, S., & Mamdani, M. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00015-6 · Cited by 7
Panopticon implications of ethical AI: equity, disparity, and inequality in healthcare
Kuiler, E. W., & McNeely, C. L. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-391919-7.00026-3 · Cited by 7
Smart Information Systems in Cybersecurity
Kevin, M., Ana, F., & Alexey, K. (2019) · ORBIT Journal · doi:10.29297/orbit.v2i2.105 · Cited by 7
Reducing drug errors by engaging student nurses in medication management simulation
Edwards, S. L., Williams, J. M., & Lee, M. (2019) · Journal of Prescribing Practice · doi:10.12968/jprp.2019.1.7.344 · Cited by 7
VPNET: Variable Projection Networks
Kovács, P., Bognár, G., Huber, C., & Huemer, M. (2021) · Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) · Cited by 7
Schemmer, M., Kühl, N., & Satzger, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.13827 · Cited by 9
“These wrong recommendations then can lead to a negative switch from a previously correct human decision [35].”
BackgroundHuman Discernment of Algorithmic Errors: A Case Study in Child WelfarePreprint
Fogliato, R., De‐Arteaga, M., & Chouldechova, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4050125 · Cited by 7
Are the Pilots Onboard? Equipping Radiologists for Clinical Implementation of AI
Shafique, U., Chaudhry, U. S., & Towbin, A. J. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00892-z · Cited by 6
“…bias can be reduced by turning on the AI algorithm after independent interpretation, decreasing the prominence of algorithm output, providing algorithm confidence with a result, providing the rationale for the interpretation, and varying the reliance of the observer on the automation [22, 23].”
BackgroundBeyond Mere Algorithm Aversion: Are Judgments About Computer Agents More Variable?
Buder, J., Becker, F., Bareiß, J., & Huff, M. (2024) · Communication Research · doi:10.1177/00936502241303588 · Cited by 8
Vicente, L., Matute, H., Fregosi, C., & Cabitza, F. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103474 · Cited by 14
Bak, M., Hartman, L. A., Graafland, C. H., Korfage, I. J., Buyx, A., & Schermer, M. (2025) · JMIR Cancer · doi:10.2196/65566 · Cited by 6
“This is akin to what some scholars have called the automation bias of humans regarding automated systems—people tend to rely too heavily on automated systems such as AI technologies and forget the cultural and ideological choices that were made during the development of that system [26,27].”
Human-AI Complementarity in Diagnostic Radiology: The Case of Double Reading
Wagner, I. A., & Chakradeo, K. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00886-5 · Cited by 11
“Other strategies to mitigate automation bias include emphasizing user accountability, providing confidence scores for AI outputs, and designing interfaces that encourage critical evaluation (Goddard et al., 2011) .”
Bias in Adjudication and the Promise of AI: Challenges to Procedural Fairness
Lopes, G. (2025) · Law Technology and Humans · doi:10.5204/lthj.3812 · Cited by 15
Rheu, M. (., & Cho, J. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3719681 · Cited by 9
“There has been considerable research on automation bias in domains such as aviation and clinical decision support systems [9, 35].”
Trust Dynamics in AI-Assisted Development: Definitions, Factors, and Implications
Sabouri, S., Eibl, P., Zhou, X., Ziyadi, M., Medvidović, N., Lindemann, L., & Chattopadhyay, S. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00199 · Cited by 17
“This may occur when developers suspend critical thinking [18] due to anthropomorphizing such tools [57] or automation bias [58].”
Understanding how users may work around algorithmic bias
Overbye-Thompson, H. E., & Rice, R. E. (2025) · AI & Society · doi:10.1007/s00146-025-02498-1 · Cited by 12
``I Would Have Written My Code Differently': Beginners Struggle to Understand LLM-Generated Code
Zi, Y., Li, L., Guha, A., Anderson, C. J., & Feldman, M. Q. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3731663 · Cited by 22
‘ChatGPT can make mistakes’ warnings fail: A randomized controlled trial
Kıyak, Y. S., Çoşkun, Ö., & Budakoğlu, I. İ. (2025) · Medical Education · doi:10.1111/medu.70056 · Cited by 7
“4 In clinical education, such misplaced trust in AI-generated feedback could negatively affect not only students' learning but also patient safety.”
Šimunović, I., Rezić, K., Franić, N., Boduljak, G., Batinić, M., Jukić, I. K. D., Jelovina, I., Biočić, J., Pogorelić, Z., & Markić, J. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03258-3 · Cited by 9
“Previous studies [33, 34] have shown that when these models are integrated into medical environments, their overconfidence can bias specialists toward the model’s predictions.”
Augmented Learning for Joint Creativity in Human-GenAI Co-Creation
Luan, Y. L., Kim, Y. J., & Zhou, J. (2025) · Information Systems Research · doi:10.1287/isre.2024.0984 · Cited by 12
Evolving from Rules to Learning in Urban Modeling and Planning Support Systems
Cai, Z. (2025) · Urban Science · doi:10.3390/urbansci9120508 · Cited by 6
Temur, K. T., & Mağat, G. (2026) · Scientific Reports · doi:10.1038/s41598-025-34773-y · Cited by 7
“In such settings, hallucinated outputs may appear clinically convincing while lacking an evidence-based foundation, thereby increasing the risk of diagnostic error 18,19 .”
Critical Alliance of AI in Education: A Pedagogical Framework for Safeguarding Cognitive Skills
Ramos-Benítez, M. J., García-Osorio, M. E., & Delgado, Y. (2026) · International Medical Education · doi:10.3390/ime5010022 · Cited by 10
“Ultimately, persistent over-reliance on AI risks weakening foundational domains such as analysis, reasoning, and communication [12–14 , 19].”
Chow, M., Li, A., Quek, D. Y. J., Fan, E. B., & Sadasiv, M. S. (2026) · Postgraduate Medical Journal · doi:10.1093/postmj/qgag068 · Cited by 6
Research Interpretation of Article 14 of the EU AI Act: Human in Command in Manufacturing
Aschenbrenner, D., Jantzen, L., Colloseus, C., Sell, A., & Kempen, R. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-71629-4_16 · Cited by 6
The Digital Siren’s Call: Accepting Unethical AI Advice
Xu, C., Xu, H., Sun, Y.-Q., & Xiong, W. (2024) · International journal of human computer interactions · doi:10.1080/10447318.2024.2400396 · Cited by 9
“Goddard et al. (2012) suggested that automation bias—the tendency to over-rely on automated outputs—may be consistent across age groups.”
The Hard Problem of AI Alignment: Value Forks in Moral Judgment
Kneer, M., & Viehoff, J. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732174 · Cited by 8
Evaluation of Biomedical and Health Information Resources
Friedman, C. P., & Wyatt, J. C. (2013) · Springer eBooks · doi:10.1007/978-1-4471-4474-8_11 · Cited by 6
Self-citationCohen, M. R., & Smetzer, J. L. (2017) · Hospital Pharmacy · doi:10.1310/hpj5201-7 · Cited by 6
A qualitative investigation of healthcare workers’ strategies in response to readmissions
Pennathur, P. R., & Ayres, B. S. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-2945-9 · Cited by 6
“over-reliance on automation can lead to complacency and errors [69].”
BackgroundThe Evolution Is Now: Service Robots, Behavioral Bias and Emotions
Letheren, K., Russell‐Bennett, R., Whittaker, L., Whyte, S., & Dulleck, U. (2020) · Research on emotion in organizations · doi:10.1108/s1746-979120200000016005 · Cited by 6
Calibrating trust toward an autonomous image classifier.
Ingram, M., Moreton, R., Gancz, B., & Pollick, F. E. (2021) · Technology Mind and Behavior · doi:10.1037/tmb0000032 · Cited by 6
“Some examples of these biases include: Automation Bias, where automation performance is perceived as inherently superior to human performance (Goddard et al., 2011); and Perfect Automation Schema, where individuals may believe that automation is almost always perfectly reliable (Dzindolet et al.,…”
BackgroundDesign Thinking Framework for Integration of Transparency Measures in Time-Critical Decision Support
Stone, P. B., Jessup, S. A., Ganapathy, S., & Harel, A. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2068745 · Cited by 7
“…instances of automation bias, researchers have recommended training, providing users with the agent ’ s reasoning process, and determining an appropriate level of automation for the agent to ensure the human remains in-the-loop enough so that SA is maintained (Cummings, 2004; Goddard et al., 2012).”
BackgroundCognitive and Behavioral Impacts of Two Decision-Support Modes for Judgmental Bootstrapping
Marois, A., Labonté, K., Lafond, D., Neyedli, H. F., & Tremblay, S. (2023) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434231153311 · Cited by 6
“, 2015; Dekker & Woods, 1999) but, on the other hand, overexposure and high levels of automation can also lead to overconfidence and to complacency (Bahner et al., 2008; Goddard et al., 2012; Yan et al., 2011).”
BackgroundLewaherilla, N. C., & Huwae, V. E. (2023) · Jurnal Minfo Polgan · doi:10.33395/jmp.v12i1.12537 · Cited by 6
“The implementation of automated selection processes mitigates potential subjective biases that may arise in manual selection procedures (Goddard et al., 2012).”
BackgroundDimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation
Woodland, M. E., Patel, N., Taie, M. A., Yung, J. P., Netherton, T., Patel, A., & Brock, K. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44336-7_15 · Cited by 6
“can be reduced by displaying low confidence values for recommendations that are likely incorrect [3].”
BackgroundArtificial intelligence challenging core State functions
Rangone, N. (2023) · Revista de Derecho Público Teoría y método · doi:10.37417/rdp/vol_8_2023_1949 · Cited by 13
Artificial intelligence and the future of pharmacy
Nelson, S. D. (2023) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxad316 · Cited by 7
Clinical decision support in the neonatal ICU
Rao, A. V., & Palma, J. P. (2022) · Seminars in Fetal and Neonatal Medicine · doi:10.1016/j.siny.2022.101332 · Cited by 6
Cabitza, F., Campagner, A., Zotti, F. D., & Sternini, A. R. A. F. (2020) · IADIS International Conference on e-Health (EH) · doi:10.33965/eh2020_202009l020 · Cited by 6
“…their decisions, when these are made in light of the machine’s recommendations: suffice it here to mention the effects known as “automation bias” (Goddard et al., 2012), that is over-relying on the machine’s advice believing that it will never (or very seldom) be wrong, “automation…”
BackgroundThe Effect of AI-generated Advice on Decision-Making in Personnel SelectionPreprint
Cecil, J., Lermer, E., Hudecek, M. F. C., Sauer, J., & Gaube, S. (2023) · doi:10.31219/osf.io/349xe · Cited by 6
The Role of Personality in Trust in Public Policy Automation
Waggoner, P., & Kennedy, R. (2022) · Journal of Behavioral Data Science · doi:10.35566/v2n1/p4/ · Cited by 7
“In line with research finding high levels of trust in algorithms (Goddard, Roudsari, & Wyatt, 2011), the significant conditioning role of these personality inventories suggests that personalities associated with intellectual curiosity, agreeableness, openness to advice-givers, as well as being…”
Result
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 ›
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