Supporting Trust Calibration and the Effective Use of Decision Aids by Presenting Dynamic System Confidence Information

By John M. McGuirl, Nadine B. Sarter. 2006

217 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Social Psychology

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Citations in the corpus

Almost human: Anthropomorphism increases trust resilience in cognitive agents. (2016, background): “Efforts to improve relationships with machines have instead focused on using design and training to enhance trust calibration (Bahner et al., 2008; Beller, Heesen, & Vollrath, 2013; de Visser, Cohen, Freedy, & Parasuraman, 2014; Helldin, Falkman, Riveiro, & Davidsson, 2013; McGuirl & Sarter, 2006).”
Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams (2019, background): “\Convey uncertainty directly Conveying uncertainty directly by providing facial cues [8] or machine confidence indicators [36,57,62,86,97,157]”
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making (2020, background): “Consistent with prior work on trust calibration for automation [20], we show that case speciic conidence information can improve trust calibration in AI-assisted decision making scenarios.”
Affective Processes in Human–Automation Interactions (2011, background): “Scholars suggest perceptions of machine reliability are crucial and that trust must be correctly “calibrated” with actual machine reliability (e.g., McGuirl & Sarter, 2006; Merritt & Ilgen, 2008; Rovira, McGarry, & Parasuraman, 2007; Wang et al., 2009; Wiegmann, 2002).”
Are Well-Calibrated Users Effective Users? Associations Between Calibration of Trust and Performance on an Automation-Aided Task (2015, methodology): “This operationalization is consistent with the definition of calibration proposed by Parasura-man and Miller (2004), who suggested that users should possess accurate beliefs about the auto-mation’s reliability, and it has been used in some past research (e.g., McGuirl & Sarter, 2006).”
From ‘automation’ to ‘autonomy’: the importance of trust repair in human–machine interaction (2018, background): “For initial ideas and frameworks, we focus in this improving human–automation trust calibration (e.g. McGuirl and Sarter 2006; de Visser et al. 2014), enhancing the general transparency of the system (Koo et al. 2014; Mercado et al. 2016), communicating intent (Schaefer et al. 2017), conveying…”
Complacency and Bias in Human Use of Automation: An Attentional Integration (2010, background): “Sarter and Schroeder (2001; see also McGuirl & Sarter, 2006) examined the performance of pilots interacting with automated decision aids that supported decision making in case of in-flight icing events.”
I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI (2021, background): “Even though the IOED itself received little attention in the context of intelligent systems and XAI, there is prior research on cognitive biases of explanations from intelligent systems investigating automation [9, 31, 40, 49, 52], anchoring [19, 27, 57], framing [26], and confirmation biases [23, 5”
Developing Decision Aids to Enable Human Spaceflight Autonomy (2017, background): “Previous research indicates that if human operators lack trust in the system, they will not use it (McGuirl and Sarter, 2006).”
Automation bias and verification complexity: a systematic review (2017, background): “Tracking task Smart icing system Multi Recover from inflight icing events D, T DS (40) 30 flight instructors 28 part flights – Not reported”
Situation awareness-based agent transparency and human-autonomy teaming effectiveness (2018, background): “Prior research has shown the positive effects of agents conveying uncertainty on joint human – agent team performance effectiveness and operator perceptions of agent trustworthiness (Beller, Heesen, and Vollrath 2013; McGuirl and Sarter 2006; Mercado et al. 2016).”