Individual Differences in the Calibration of Trust in Automation

By Vlad L. Pop, Alex Shrewsbury, Francis T. Durso. 2015

128 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Social Psychology

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In Experimental and Cognitive Psychology

In Social Psychology

Citations in the corpus

Metrics for Explainable AI: Challenges and Prospects (2018, background): “…et al., 2015; Hoff and Bashir 2015; Fitzhugh, Hoffman & Miller, 2011; Hoffman et al., 2009, 2013; Huynh et al., 2006; Kucala, 2013; Lee & See, 2004; Merritt and Ilgen, 2008; Merritt et al. 2013, 2015; Naone, 2009; Pop et al. 2015; Shadbolt, 2002; Wickens et al., 2015; Woods and Hollnagel 2006).”
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making (2020, background): “While the concept of trust calibration has been studied for automation [13, 18, 20, 26], as to prevent both automation aversion and automation bias, it is not well understood”
A Taxonomy of Emergent Trusting in the Human–Machine Relationship (2017, unknown): “…concern in computer science and cognitive systems engineering (e.g., Chancey et al. 2015; Hoff and Bashir 2015; Hoffman et al. 2009; Huynh et al. 2006; Naone 2009; Merritt and Ilgen 2008; Merritt et al. 2013, 2015a; Pop et al. 2015; Shadbolt 2002; Wickens et al. 2015; Woods and Hollnagel 2006).”
Explanations Can Reduce Overreliance on AI Systems During Decision-Making (2022, unknown): “The effect of a mental model of AI accuracy is unclear, as some studies found that any accuracy score increases trust [55], others found high accuracy scores increased trust and low accuracy scores decreased trust [96, 97], while another found that these effects are mediated by an individual’s innat”
To Trust or to Think (2021, background): “Thus, numerous studies have highlighted the importance of calibrated trust in improving AI-assisted decision-making [7, 9, 31, 44, 51, 53, 67].”