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.
“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]
The Background defines automation bias as accepting computer output in place of vigilant information seeking, and the Experience section says task-inexperienced users are more prone to it. The Overall Conclusion links appropriate reliance to calibrated trust and accountability. The paper does not establish that trainees cannot distinguish appropriate delegation from uncritical acceptance, or characterize this as an abdication of epistemic responsibility. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAutomation 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.
“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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredPost-hoc interaction with single-pass AI 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.
“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]
The Experience section (page not available) reports that automation bias occurs more often among task-inexperienced users, while the Confidence and trust section links trust in decision-support systems to over-reliance. The paper does not establish that single-pass AI outputs are opaque or coarse-grained, or that experts must reverse-engineer model reasoning to correct errors. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThese findings relate to work on automation bias, algorithm aversion, and appropriate reliance.
“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]
The paper's Background discusses automation bias, and its “Automation bias avoidance Implementation factors” section explicitly discusses appropriate reliance. The cited sentence also names algorithm aversion, which our copy does not address; the sentence does not specify which of its four citations supports that term. (LLM, conf=0.84)
Experimental — accuracy not yet measuredHR 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.
“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
The Experience and Confidence and trust sections report that experience may increase users’ confidence, that greater confidence in one’s own decisions decreases reliance on decision support, and that less experienced users can be more prone to automation bias. The review does not establish this relationship specifically for HR professionals or personnel selection tasks. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Deliberately programming errors into AI-based decision support systems and pointing them out when they are overlooked can support the attention of decision makers by varying reliability over time.
“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
The “Design of DSS” section says varying reliability rather than keeping it constant increased vigilant behavior and improved appropriate reliance, supporting the citing sentence’s point about attention. It does not say to deliberately program errors into a system or point out errors that decision makers overlook. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAutomation bias has been found to be a serious problem in contexts of AI-based decision support systems.
“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
The Background says automation bias can cause commission and omission errors when people use clinical decision support systems, and that decision errors can have severe consequences. The paper addresses computerized decision support broadly; these passages do not establish the claim specifically for AI-based systems. (LLM, conf=0.78)
Experimental — accuracy not yet measuredGoddard et al. (2012) also deemed expertise, trust, and self-confidence influential in a systematic review.
“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
Automation bias occurs in algorithmic decision-making: a human being relies too heavily on the algorithm, even in the face of erroneous predictions.
“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
Goddard et al. (2012) propose that expertise may act on primary drivers (trust and confidence) to influence reliance on DSS.
“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
Expertise has been shown to reduce trust and reliance on AI, even when the user's judgement is incorrect.
“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
Heightened confidence in one's own decisions generally diminishes reliance on external support.
“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
Automation bias is the tendency to over-rely on algorithmic output, leading clinicians to accept erroneous recommendations without adequate scrutiny, and this risk is amplified by automation bias.
“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 principle of non-discrimination in algorithmic decision-making translates into a duty to avoid so-called automation biases, preventing outcomes that might be discriminatory toward the addressee of administrative action.
“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)
Experimental lab studies have diagnosed automation bias across a number of research fields.
“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
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.
“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
Automation bias has been found to lead to adverse effects on decision outcomes in several other contexts.
“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
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.
“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
Human involvement does not necessarily guarantee effective control.
“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 tendency to defer to AI is consistent with automation bias, in which people overrely on automated recommendations and accept them without sufficient independent verification.
“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
All 41 Study 2 users had previously passed the authors' standard psychological screener [30].
“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]
The Screening and eligibility section describes screening papers for a systematic review, not screening study participants. Searches for “standard psychological screener” and “41 Study 2 users” found no passage in our copy addressing whether those users passed a screener (page numbers unavailable). (LLM, conf=0.76)
Experimental — accuracy not yet measuredAutomation 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.
“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]
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.
“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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See Something, Say Something: Correction of Global Health Misinformation on Social Media
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“Finally, although research suggests trust in algorithms is relatively high, boosting their effectiveness as mechanisms of correction (Goddard et al., 2012), such trust can also be threatened.”
Background'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173951 · Cited by 691 · see citing works
“Various research suggests that users—again, primarily experts— may in certain cases systematically over-rely on the outputs of intelligent systems [26, 19], while in others they may systematically distrust systems, despite reassurances as to their accuracy [57, 24].”
BackgroundThe Ethical Implications of Using Artificial Intelligence in Auditing
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“Failure to detect incorrect AI advice may otherwise result in wrong diagnoses and numerous medical errors (Tsai et al. 2003, Alberdi et al. 2004, Goddard et al. 2012).”
BackgroundTowards a Theory of Longitudinal Trust Calibration in Human–Robot Teams
de Visser, E. J., Peeters, M. M. M., Jung, M. F., Kohn, S., Shaw, T. H., Pak, R., & Neerincx, M. A. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-019-00596-x · Cited by 447 · see citing works
“Many examples exist of accidents caused by overtrust [6,45,111,133].”
BackgroundExplainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities
Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2020) · Information Systems Management · doi:10.1080/10580530.2020.1849465 · Cited by 426 · see citing works
“In certain situations, humans have a tendency to over-rely on automated decision-making, called “automation bias”, which can result in a potential failure to recognize errors in the black box (Goddard et al., 2012).”
BackgroundDratsch, T., Chen, X., Rezazade Mehrizi, M., Kloeckner, R., Mähringer-Kunz, A., Püsken, M., Baeßler, B., Sauer, S., Maintz, D., & Pinto dos Santos, D. (2023) · Radiology · doi:10.1148/radiol.222176 · Cited by 369 · see citing works
Inherent Tradeoffs in the Fair Determination of Risk Scores
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603197 · Cited by 347 · see citing works
The role of trust in human-robot interaction
Lewis, M., Sycara, K., & Walker, P. M. (2018) · Studies in systems, decision and control · doi:10.1007/978-3-319-64816-3_8 · Cited by 298 · see citing works
Trust and medical AI: the challenges we face and the expertise needed to overcome them
Quinn, T. P., Senadeera, M., Jacobs, S., Coghlan, S., & Le, V. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa268 · Cited by 287 · see citing works
Artificial Intelligence and Patient-Centered Decision-Making
Bjerring, J. C., & Busch, J. (2021) · Philosophy & Technology · doi:10.1007/s13347-019-00391-6 · Cited by 274 · see citing works
Cave, S., & Dihal, K. (2020) · Philosophy & Technology · doi:10.1007/s13347-020-00415-6 · Cited by 279 · see citing works
Chong, L., Zhang, G., Goucher-Lambert, K., Kotovsky, K., & Cagan, J. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107018 · Cited by 262 · see citing works
Automation Bias and Assistive AI: Risk of Harm From AI-Driven Clinical Decision Support
Khera, R., Simon, M. A., & Ross, J. S. (2023) · JAMA · doi:10.1001/jama.2023.22557 · Cited by 222 · see citing works
A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
De-Arteaga, M., Fogliato, R., & Chouldechova, A. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376638 · Cited by 221 · see citing works
“Studies analyzing factors contributing to automation bias have found that complex tasks and time pressure may increase over-reliance on decision support [41, 17].”
BackgroundStock‐Homburg, R. (2021) · International Journal of Social Robotics · doi:10.1007/s12369-021-00778-6 · Cited by 199 · see citing works
Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations
Schemmer, M., Kuehl, N., Benz, C., Bartos, A., & Satzger, G. (2023) · Proceedings of the 28th International Conference on Intelligent User Interfaces · doi:10.1145/3581641.3584066 · Cited by 208 · see citing works
“on automation [4, 24, 54] and, therefore, should decrease the RSR.”
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Exploring automation bias in human–AI collaboration: a review and implications for explainable AI
Romeo, G., & Conti, D. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02422-7 · Cited by 209 · see citing works
“Additionally, accountability, which has been proposed as an intervention to mitigate AB (Goddard et al. 2012), lacks empirical support.”
InfluentialRapid Trust Calibration through Interpretable and Uncertainty-Aware AI
Tomsett, R., Preece, A., Braines, D., Cerutti, F., Chakraborty, S., Srivastava, M., Pearson, G., & Kaplan, L. (2020) · Patterns · doi:10.1016/j.patter.2020.100049 · Cited by 186 · see citing works
Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168 · see citing works
Humans inherit artificial intelligence biases
Vicente, L., & Matute, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-42384-8 · Cited by 158 · see citing works
Scherbakov, D., Hubig, N., Jansari, V., Bakumenko, A., & Lenert, L. A. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf063 · Cited by 154 · see citing works
Choudhury, A., & Chaudhry, Z. (2024) · Journal of Medical Internet Research · doi:10.2196/56764 · Cited by 159 · see citing works
“A doctor with high trust (blind trust) in the LLM, might be more inclined to accept its suggestion without extensive further verification [25,26], exhibiting automation bias [27].”
Freyer, O., Wiest, I. C., Kather, J. N., & Gilbert, S. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00124-9 · Cited by 162 · see citing works
Hallucination‐Free? Assessing the Reliability of Leading <scp>AI</scp> Legal Research Tools
Magesh, V., Surani, F., Dahl, M., Suzgun, M., Manning, C. D., & Ho, D. E. (2025) · Journal of Empirical Legal Studies · doi:10.1111/jels.12413 · Cited by 182 · see citing works
The four dimensions of contestable AI diagnostics - A patient-centric approach to explainable AI
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Automation bias: Empirical results assessing influencing factors
Goddard, K., Roudsari, A., & Wyatt, J. C. (2014) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2014.01.001 · Cited by 163 · see citing works
Self-citationEthical impact of artificial intelligence in managerial accounting
Zhang, C., Zhu, W., Dai, J., Wu, Y., & Chen, X. (2023) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2023.100619 · Cited by 150 · see citing works
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The role of explainable AI in the context of the AI Act
Panigutti, C., Hamon, R., Hupont, I., Llorca, D. F., Yela, D. F., Junklewitz, H., Scalzo, S., Mazzini, G., Sánchez, I., Garrido, J. S., et al. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594069 · Cited by 171 · see citing works
“An inappropriate level of trust can lead to over-reliance on automation, a phenomenon known as automation bias [46, 66].”
BackgroundA Computer Science Perspective on Digital Transformation in Production
Brauner, P., Dalibor, M., Jarke, M., Kunze, I., Koren, I., Lakemeyer, G., Liebenberg, M., Michael, J., Pennekamp, J., Quix, C., et al. (2022) · ACM Transactions on Internet of Things · doi:10.1145/3502265 · Cited by 145 · see citing works
Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging · doi:10.1117/1.jmi.10.6.061104 · Cited by 149 · see citing works
“This can include a loss of awareness over what automation is and is not taking care of and its limitations.(64) Attention plays a major role in this bias; it is worsened when multiple-task overload (emergency), complexity, and/or fatigue is present in the human expert.”
BackgroundFormosa, P. (2021) · Minds and Machines · doi:10.1007/s11023-021-09579-2 · Cited by 144 · see citing works
Understanding and Mitigating Bias in Imaging Artificial Intelligence
Tejani, A. S., Ng, Y. S., Xi, Y., & Rayan, J. C. (2024) · RadioGraphics · doi:10.1148/rg.230067 · Cited by 138 · see citing works
Abdelwanis, M., Alarafati, H. K., Tammam, M. M. S., & Simsekler, M. C. E. (2024) · Journal of Safety Science and Resilience · doi:10.1016/j.jnlssr.2024.06.001 · Cited by 137 · see citing works
Human–Vehicle Cooperation in Automated Driving: A Multidisciplinary Review and Appraisal
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“…Beggiato, Pereira, Petzoldt, and Krems (2015) and Sullivan, Flannagan, Pradhan, and Bao (2016) also point at drivers ’ fragmented knowledge of assistance systems ’ capabilities and limitations as a determinant in accelerating the process leading to system misuse (Goddard, Roudsari, & Wyatt, 2012).”
BackgroundWhy trust an algorithm? Performance, cognition, and neurophysiology
Alexander, V., Blinder, C., & Zak, P. J. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.026 · Cited by 135 · see citing works
“Future studies should vary the task difficulty, in addition to directly measuring cognitive load, since the former has been shown to increase algorithm adoption (Alvarado-Valencia & Barrero, 2014; Goddard et al., 2012).”
MethodPanigutti, C., Beretta, A., Giannotti, F., & Pedreschi, D. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502104 · Cited by 145 · see citing works
“This finding might be in line with previous research on automation bias in medicine, i.e., the tendency to over-rely on automation [34, 40, 49], and will definitely be the subject of future works.”
Resultpanel, G. E., de Velde, S. V., Kunnamo, I., Roshanov, P. S., Kortteisto, T., Aertgeerts, B., Vandvik, P. O., & Flottorp, S. A. (2018) · Implementation Science · doi:10.1186/s13012-018-0772-3 · Cited by 131 · see citing works
Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation
Schwartz, I. S., Link, K. E., Daneshjou, R., & Cortés-Penfield, N. (2024) · Clinical Infectious Diseases · doi:10.1093/cid/ciad633 · Cited by 131 · see citing works
“Automated bias is well documented in healthcare [22].”
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Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
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Westphal, M., Vössing, M., Satzger, G., Yom‐Tov, G. B., & Rafaeli, A. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107714 · Cited by 141 · see citing works
Bond, R. R., Novotny, T., Andrsova, I., Koc, L., Sisakova, M., Finlay, D., Guldenring, D., McLaughlin, J., Peace, A., McGilligan, V., et al. (2018) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2018.08.007 · Cited by 127 · see citing works
Larsen, M., Aglen, C. F., Lee, C. I., Hoff, S. R., Lund-Hanssen, H., Lång, K., Nygård, J. F., Ursin, G., & Hofvind, S. S. H. (2022) · Radiology · doi:10.1148/radiol.212381 · Cited by 120 · see citing works
“The effect of being presented with a high AI score may lead to overreliance on the AI system without a radiologist maintaining their own vigilance or lead to reduced attention to other suspicious areas (automation bias) (28).”
BackgroundChugunova, M., & Sele, D. (2022) · Journal of Behavioral and Experimental Economics · doi:10.1016/j.socec.2022.101897 · Cited by 118 · see citing works
Jones, C. H. D., Thornton, J. R., & Wyatt, J. C. (2023) · Medical Law Review · doi:10.1093/medlaw/fwad013 · Cited by 122 · see citing works
Self-citationFairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey
Fabris, A., Baranowska, N., Dennis, M. J., Graus, D., Hacker, P., Saldivar, J., Zuiderveen Borgesius, F., & Biega, A. J. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3696457 · Cited by 117 · see citing works
A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning
McCradden, M. D., Anderson, J. A., Stephenson, E. A., Drysdale, E., Erdman, L., Goldenberg, A., & Shaul, R. Z. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2021.2013977 · Cited by 117 · see citing works
The unintended consequences of health information technology revisited
Ash, J., Berg, M., & Coiera, E. (2016) · Yearbook of Medical Informatics · doi:10.15265/iy-2016-014 · Cited by 101 · see citing works
“Of specific interest to decision support system use, automation biases or automation-induced complacency are increasingly recognized as an important source of decision error [52, 53].”
Automated Contouring and Planning in Radiation Therapy: What Is ‘Clinically Acceptable’?
Baroudi, H., Brock, K. K., Cao, W., Chen, X., Chung, C. D., Court, L. E., Basha, M. D. E., Farhat, M., Gay, S., Gronberg, M., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13040667 · Cited by 112 · see citing works
Medical Education in the Electronic Medical Record (EMR) Era
Tierney, M. J., Pageler, N. M., Kahana, M. D., Pantaleoni, J. L., & Longhurst, C. (2013) · Academic Medicine · doi:10.1097/acm.0b013e3182905ceb · Cited by 108 · see citing works
Arnold, M. H. (2021) · Journal of Bioethical Inquiry · doi:10.1007/s11673-020-10080-1 · Cited by 105 · see citing works
Automation bias in electronic prescribing
Lyell, D., Magrabi, F., Raban, M. Z., Pont, L. G., Baysari, M. T., Day, R. O., & Coiera, E. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0425-5 · Cited by 103 · see citing works
“There are multiple possible causes of AB, [8, 9] and the literature is currently unclear regarding which, or all, of these are genuinely causal, and under which circumstances.”
BackgroundNourani, M., Kabir, S., Mohseni, S., & Ragan, E. D. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5284 · Cited by 104 · see citing works
Guiding AI in radiology: ESR’s recommendations for effective implementation of the European AI Act
Kotter, E., D’Antonoli, T. A., Cuocolo, R., Hierath, M., Huisman, M., Klontzas, M. E., Martí‐Bonmatí, L., May, M. S., Neri, E., Nikolaou, K., et al. (2025) · Insights into Imaging · doi:10.1186/s13244-025-01905-x · Cited by 103 · see citing works
Artificial intelligence in medicine and the disclosure of risks
Kiener, M. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01085-w · Cited by 97 · see citing works
“First, psychological research has shown that people have an ‘auto-mation bias’, i.e. people display an unjustified reliance on machines over human decisions and are likely to think that AI is free from the frailties of human choice (Goddard et al. 2012).”
BackgroundBrady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-023-01541-3 · Cited by 102 · see citing works
“70 – 73 These biases need to be both clari fi ed and managed to ensure the AI ’ s output does not negatively in fl uence the radiologist ’ s judgement.”
BackgroundLyell, D., Coiera, E., Chen, J., Shah, P., & Magrabi, F. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2020-100301 · Cited by 95 · see citing works
Evidence, ethics and the promise of artificial intelligence in psychiatry
McCradden, M., Hui, K., & Buchman, D. Z. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2022-108447 · Cited by 97 · see citing works
“51 Emerging evidence indicates an exacerbation of automation bias—a well-characterised problem over over-reliance on computational systems 52 —with AI systems.”
BackgroundThe loopholes of algorithmic public services: an “intelligent” accountability research agenda
Bracci, E. (2023) · Accounting, Auditing & Accountability Journal · doi:10.1108/aaaj-06-2022-5856 · Cited by 89 · see citing works
Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information?
Springer, A., & Whittaker, S. (2020) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3374218 · Cited by 84 · see citing works
“All had previously passed our standard psychological screener [30].”
BackgroundSanchez‐Martinez, S., Cámara, Ó., Piella, G., Čikeš, M., González‐Ballester, M. Á., Miron, M., Vellido, A., Gómez, E., Fraser, A. G., & Bijnens, B. H. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.765693 · Cited by 100 · see citing works
“As shown by Goddard et al. (44), when the ML solution is reliable it augments human performance, but when the solution is incorrect human errors increase.”
BackgroundFairLens: Auditing black-box clinical decision support systems
Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102657 · Cited by 86 · see citing works
Machine heuristic: concept explication and development of a measurement scale
Yang, H. J., & Sundar, S. S. (2024) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmae019 · Cited by 92 · see citing works
The Vicious Circles of Skill Erosion: A Case Study of Cognitive Automation
Rinta-Kahila, T., The University of Queensland Business School, Penttinen, E., Aalto University School of Business, Salovaara, A., Aalto University / University of Helsinki, Soliman, W., University of Agder, N. /. U. O. J., Ruissalo, J., & Aalto University School of Business (2023) · Journal of the Association for Information Systems · doi:10.17705/1jais.00829 · Cited by 86 · see citing works
Antaki, F., Milad, D., Chia, M. A., Giguère, C., Touma, S., El‐Khoury, J., Keane, P. A., & Duval, R. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2023-324438 · Cited by 83 · see citing works
“21 In our study, the grading task was multidimensional and required the graders to weight multiple factors at once.”
Wyatt, J. C. (2018) · BMC Medicine · doi:10.1186/s12916-018-1211-7 · Cited by 61 · see citing works
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The right to refuse diagnostics and treatment planning by artificial intelligence
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“It has been demonstrated [255], [256] that in many practical scenarios, human operators — especially when under time pressure and multi-tasking — quickly place too much trust in an automated system which has performed reliably in a large number of cases, thereby increasing the likelihood of a…”
BackgroundAI literacy for ethical use of chatbot: Will students accept AI ethics?
Kajiwara, Y., & Kawabata, K. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100251 · Cited by 91 · see citing works
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“Additionally, we must avoid creating a generation of physicians with automation bias [101].”
New Information Technology and Implicit Bias
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“…in new information technology appearance 5 trustworthy ” and “ humanness in new information technology performance 5 trustworthy ” indicate that potential users of these technologies hold the general belief that new information technology is nonhuman and alien (Goddard, Roudsari, & Wyatt, 2012).”
BackgroundNot all biases are bad: equitable and inequitable biases in machine learning and radiology
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“As discussed in Section 6 (Human-AI Interaction), one crucial issue is the potential introduction of human–computer biases into the radiologic interpretation (59, 70–73).”
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Explanation matters: An experimental study on explainable AI
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“This in turn can lead to undesirable effects such as “automation bias,” which describes a tendency to rely too much on automated processes (Goddard et al., 2012).”
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“Beginning students are particularly likely to err in this way: they may struggle to understand generated code, and their lack of confidence in their own abilities may make them trust the automated system over their own judgment (an example of automation bias [18, 30, 32, 91]).”
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“The investigator used the prompt to draw “detailed and accurate anatomy illustration of the human heart” on the three The inaccuracy issues may stem from DALL·E 3 possibly being trained on unrepresentative data, leading to a risk of overfitting to inaccurate disease images from automation bias [45].”
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“Overestimation was hazardous in these two cases; but it can lead to disastrous, even lethal, outcomes in critical systems and it should be avoided at all costs [57].”
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Automation bias in public administration – an interdisciplinary perspective from law and psychology
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Check the box! How to deal with automation bias in AI-based personnel selection
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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), and violates ethical recommendations (Hunkenschroer and Luetge, 2022) as well as legal requirements like Article 22 of the GDP…”
BackgroundInfluential
- 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 › Psychology › Social Psychology › 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.
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