Effect of Domestic Trainee Robots’ Errors on Human Teachers’ Trust

Aliasghari, Pourya; Ghafurian, Moojan; Nehaniv, Chrystopher L.; Dautenhahn, Kerstin

doi:10.1109/ro-man50785.2021.9515510

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…The results for the FC measure from Study 2 replicated those of Study 1, as reported in table 5; however, it should be noted that the model fits were generally poor (see the R 2 values in table 5). The DTI model (2) was no better than (1) while (3) managed to explain significantly more variance in the parameters. Adding the DTI measure to (3), again, did not improve the model's performance.…”

Section: Studymentioning

confidence: 87%

“…At the time of writing, the article introducing this measure had over 6000 citations including numerous applications in human-AI interactions (e.g. [44,45,33,46,22,34,41,2,3,31]). No publications were found that validated the scale for use in human-AI interactions settings.…”

Section: Measuring Disposition To Trustmentioning

confidence: 99%

“…There is evidence of this measure having (e.g. [33,2]) and not having (e.g. [44,34]) predictive value in interactions with both virtual and physical robots.…”

Section: Introductionmentioning

confidence: 99%

“…Sufficient transparency in communication will allow people to know when they should comply with an AI's recommendation and when they should not, i.e. develop better-calibrated trust, which is an important precursor to good compliance behavior in human-AI interactions [21,8,44,33,2].…”

Section: Introductionmentioning

confidence: 99%

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Comparing Psychometric and Behavioral Predictors of Compliance During Human-AI Interactions

Gurney¹,

Pynadath²,

Wang³

2023

Preprint

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Optimization of human-AI teams hinges on the AI's ability to tailor its interaction to individual human teammates. A common hypothesis in adaptive AI research is that minor differences in people's predisposition to trust can significantly impact their likelihood of complying with recommendations from the AI. Predisposition to trust is often measured with self-report inventories that are administered before interactions. We benchmark a popular measure of this kind against behavioral predictors of compliance. We find that the inventory is a less effective predictor of compliance than the behavioral measures in datasets taken from three previous research projects. This suggests a general property that individual differences in initial behavior are more predictive than differences in self-reported trust attitudes. This result also shows a potential for easily accessible behavioral measures to provide an AI with more accurate models without the use of (often costly) survey instruments.

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Section: Studymentioning

confidence: 87%

Section: Measuring Disposition To Trustmentioning

confidence: 99%

“…There is evidence of this measure having (e.g. [33,2]) and not having (e.g. [44,34]) predictive value in interactions with both virtual and physical robots.…”

Section: Introductionmentioning

confidence: 99%