The role of trust in automation reliance

By Mary T. Dzindolet, S. Peterson, Regina A. Pomranky, Linda G. Pierce, Hall P. Beck. 2003

1,278 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Radiological and Ultrasound Technology, Social Psychology, Human-Computer Interaction, Cognitive Science & Psychology

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “Both can playa role in producing an appropriate level of trust in the aid (Dzindolet et al., 2003, pp. 697,715).”
Metrics for Explainable AI: Challenges and Prospects (2018, background): “Trust in automation can rapidly break down under conditions of time pressure, or when there are conspicuous system faults or errors, or when there is a high false alarm rate (Dzindolet et al., 2003; Madhavan and Wiegmann 2007).”
Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020, background): “Some results regarding the tendency of explanations to cause humans to be overly trusting of conventional decision aids seem to transfer to AI-based aids, 66,67 although the effects will be dependent on the particular characteristics of the explanations provided.”
In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes (2019, background): “Trust is highly dependent on circumstances [4], and previous measurements of trust (e.g., [7, 10, 19, 28]) have largely relied on subjective assessments of what they believed influenced trust in specific situations.”
RISE: Randomized Input Sampling for Explanation of Black-box Models (2018, background): “There is extensive evidence for the importance of explanation towards understanding and building trust in cognitive psychology [15], philosophy [16] and machine learning [6, 14, 21] research.”
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations (2019, background): “For machine learning systems to be widely adopted in practice, they need to be trusted by users [10].”
'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].”
Transparency and trust in artificial intelligence systems (2020, background): “Moreover, trust in ML is found to build up slower and to decrease faster than trust in humans (Dzindolet et al., 2003).”
Human Performance Consequences of Automated Decision Aids (2012, background): “Evidence for this assumption is provided by many studies pointing to strong effects of automation failures on operators’ trust and reliance (e.g., de Vries et al., 2003; Dzindolet et al., 2003; Lee & Moray, 1992, 1994; Madhavan, Wiegman, & Lacson, 2006).”
Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems (2020, background): “explainable interfaces should be instilling in users the right amount of trust [10, 32, 33].”
Situation Awareness, Mental Workload, and Trust in Automation: Viable, Empirically Supported Cognitive Engineering Constructs (2008, background): “…interrelationships between trust and factors such as automation reliability, error type, task difficulty, and other factors (Bisantz & Seong, 2001; Dzindolet, Peterson, Pomranky, Pierce, & Beck, 2003; Dzindolet, Pierce, Beck, Dawe, & Variations in trust are one factor in an operator’s use of…”
Principles of Explanatory Debugging to Personalize Interactive Machine Learning (2015, background): “Increased transparency has been associated with many beneﬁts, including increased user acceptance of (or satisfaction with) recommendations and predictions [4, 8, 42], improved training of intelligent agents [41], and increased trust in the system’s predictions [10].”
Not All Trust Is Created Equal: Dispositional and History-Based Trust in Human-Automation Interactions (2008, methodology): “, 2003), a visual search task (Dzindolet et al., 2003), the PASTEURIZER task (Lee & Moray, 1992), and others (Johnson, 2004; Moray, Inagaki, & Itoh, 2000; Wiegmann, Rich, & Zhang, 2001).”
Toward a framework for levels of robot autonomy in human-robot interaction (2014, background): “A number of models and theories related to trust in automation (Cohen, Parasuraman, & Freeman, 1998; Dzindolet et al., 2003; Lee & See, 2004; Madhavan & Wiegmann, 2007) and preliminary frameworks of trust in HRI have been proposed (Desai, Stubbs, Steinfeld, & Yanco, 2009; Hancock, Billings, & Schaef”
Impact of robot failures and feedback on real-time trust (2013, background): “Similar attempts to understand how robot operators trust and utilize automated behaviors of robots have been made in the .eld of humanrobot interaction (HRI) (for a survey and analysis of recent research see [19]).”
Trust in Automation (2015, background): “Evidence suggests that people often exhibit a positivity bias in their trust of novel automated systems (Dzindolet, Peterson, Pomranky, Pierce, & Beck, 2003).”