Complacency and Bias in Human Use of Automation: An Attentional Integration

By Raja Parasuraman, Dietrich Manzey. 2010

1,355 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Social Psychology

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Citations in the corpus

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020, background): “57,58 It occurs when humans accept computer outputs in place of their own thinking and judgment, leading them to place too much trust in algorithmic outputs.”
Implementing Lumberjacks and Black Swans Into Model-Based Tools to Support Human–Automation Interaction (2017, methodology): “Although there are many ways any given automation system can fail, based on the work of Mosier, Skitka, Heers, and Burdick (1998) and Parasuraman and Manzey (2010), an important dichotomy built into our S-PRINT model of automation failure detection was whether automation decision aid failed because…”
Human Trust in Artificial Intelligence: Review of Empirical Research (2020, background): “Hoff and Bashir (2015) posited that the way trust in technology unfolds differs from the way it develops in humans because of the common positivity bias toward new technologies (Parasuraman & Manzey, 2010).”
A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction (2011, background): “…levels of trust may have negative consequences, such as overreliance on and misuse of the system (in cases of extremely high levels of trust) or dis-use of the system entirely (in cases of very low levels of trust) (Lee & See, 2004; Parasuraman & Manzey, 2010; Parasuraman & Riley, 1997).”
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].”
Acceptance of Automated Road Transport Systems (ARTS): An adaptation of the UTAUT model (2016, neutral): “…to use any automated system is based on a number of attitudinal factors including trust (e.g. Ghazizadeh, Lee & Boyle, 2012), workload (e.g. Parasuraman & Manzey, 2010), perceived usefulness and ease of use (e.g. Davis, 1989), and social influence (e.g. Venkatesh, Morris, Davis & Davis,…”
The 3 C's of anthropomorphism: Connection, comprehension, and competition (2019, background): “They expect artificial minds to be highly competent and close to perfect in executing calculable tasks and making predictions (Dzindolet, Peterson, Pomranky, Pierce, & Beck, 2003; Parasuraman & Manzey, 2010).”
Augmenting Medical Diagnosis Decisions? An Investigation into Physicians' Decision-Making Process with Artificial Intelligence (2021, background): “This stream suggests that incorrect system advice is extremely problematic because decision makers often fail to detect occasional errors of highly accurate systems (Parasuraman and Manzey 2010, Goddard et al. 2012, Endsley 2017).”
Artificial Intelligence and Management: The Automation–Augmentation Paradox (2020, background): “Prior research has shown that automation can deskill humans, make them complacent, and diffuse their sense of responsibility (Parasuraman & Manzey, 2010; Skitka, Mosier, & Burdick, 2000).”
Understanding human management of automation errors (2013, background): “Parasuraman and Manzey (2010) also argued that complacent behaviour needs to be evaluated independently of detection performance.”
Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function) (2018, background): “Cases of particular concern are those where ML or algorithmic heuristics are deployed in decision support tools that humans are inadequately inclined to question.(Parasuraman and Manzey 2010)”
RADAR - A Proactive Decision Support System for Human-in-the-Loop Planning (2017, background): “(Parasuraman and Manzey 2010) shows that human use of automation may result in automation bias leading to omission and commission errors, which underlines the importance of reliability of the automation (Parasuraman and Riley 1997).”