Automation bias: decision making and performance in high-tech cockpits.

By K. Mosier, Linda J. Skitka, S. Heers, Mark Burdick. 1997

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Citations in the corpus

Comparing Trust in Auto-GCAS Between Experienced and Novice Air Force Pilots (2017, background): “Yet, research has shown that experienced pilots are also vulnerable to automation bias (Mosier et al., 1998); thus, the potential for overreliance is not simply an issue of concern for younger pilots.”
Misuse of automated decision aids: Complacency, automation bias and the impact of training experience (2008, background): “…from accident reports in aviation and from laboratory studies using multiple-task scenarios which simulate typical demands of cockpit work on different levels of fidelity (e.g., Parasuraman et al., 1993; Mosier et al., 1998; Funk et al., 1999; Skitka et al., 1999, 2000a; Prinzel et al., 2001).”
Automation Bias and Errors: Are Crews Better Than Individuals? (2000, unknown): “Although this study is limited by the fact that a student sample was used, previous research has found that omission and commission error rates are quite constant across student and professional pilot samples (Mosier & Skitka, 1996; Mosier et al., 1997, 1998).”
Complacency and Bias in Human Use of Automation: An Attentional Integration (2010, methodology): “In another study, Mosier et al. (1998) had experienced commercial pilots perform a simulated flight in a trainer equipped with advanced cockpit automation systems (e.g., Flight Management System, EICAS).”
Automation bias and verification complexity: a systematic review (2017, background): “EICAS event (engine fire) M, D, T A, DS (39) 25 commercial pilots 2 part flights Not reported Not reported”
Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (2018, background): “Canonical domains in human factors have also focussed on even more dramatic life-and-death situations, such as issues around decision support in airplane cockpits [57] and air traffic control [37].”
Progressive Disclosure: Designing for Effective Transparency (2018, background): “Users are prone to blindly believing outputs from algorithmic systems, a phenomena referred to as algorithmic omniscience [23,36,74] and automation bias [17,56].”
Understanding human management of automation errors (2013, background): “Mosier et al. (1998) conducted one of the ﬁrst endeavours in understanding the role of accountability in the context of human–automation interaction.”
The Automation-by-Expertise-by-Training Interaction (2017, background): “Researchers have also described a unique error, automation bias, that results from extended operator use of and reliance upon automation (e.g., Mosier & Skitka, 1996; Mosier, Skitka, Heers, & Burdick, 1998; Parasuraman & Riley, 1997; Skitka & Mosier, 2000).”