Automation bias: a systematic review of frequency, effect mediators, and mitigators

By Kate Goddard, A. Roudsari, J. Wyatt. 2012

859 citations recorded by the source corpus.

Subfields: Social Psychology, Surgery, Safety, Robustness & Interpretability

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Citations in the corpus

WebGPT: Browser-assisted question-answering with human feedback (2021, background): “In combination with the well-documented problem of “automation bias” [Goddard et al., 2012], this could lead to overreliance on WebGPT’s answers.”
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018, background): “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].”
An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence (2017, background): “What happens when we let what Goddard, Roudsari, and Wyatt (2012) called automation bias run rampant?”
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (2024, neutral): “Because an ungrounded response does not provide key information (supporting authorities) that the user needs, it is marked incomplete.”
Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities (2020, background): “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).”
See Something, Say Something: Correction of Global Health Misinformation on Social Media (2018, background): “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.”
Why trust an algorithm? Performance, cognition, and neurophysiology (2018, methodology): “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).”
Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams (2019, background): “Many examples exist of accidents caused by overtrust [6,45,111,133].”
Augmenting Medical Diagnosis Decisions? An Investigation into Physicians' Decision-Making Process with Artificial Intelligence (2021, background): “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).”