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Towards Warranted Trust: A Model on the Relation Between Actual and Perceived System Trustworthiness

Schlicker, Nadine, Langer, Markus·2021·Mensch und Computer 2021
DOI 10.1145/3473856.3474018
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Abstract

The public discussion about trustworthy AI is fueling research on new methods to make AI explainable and fair. However, users may incorrectly assess system trustworthiness and could consequently overtrust untrustworthy systems or undertrust trustworthy systems. In order to understand what determines accurate assessments of system trustworthiness we apply Brunswik’s Lens Model and the Realistic Accuracy Model. The assumption is that the actual trustworthiness of a system cannot be accessed directly and is therefore inferred via cues to form a user’s perceived trustworthiness. The accuracy of trustworthiness assessment then depends on: cue relevance, availability, detection, and utilization. We describe how the model can be used to systematically investigate determinants that increase the match between system’s actual trustworthiness and user’s perceived trustworthiness in order to achieve warranted trust.

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Crossref · 10.1145/3473856.3474018

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Citations

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Metadata

Authors
Schlicker, N., & Langer, M.
Affiliations
  • Universitätsklinikum Gießen und Marburg, Phillips-Universität Marburg, Germany
  • Universität des Saarlandes, Germany
Venue
Mensch und Computer 2021
Publisher
ACM
Published
2021-09-05
Pages
325-329
DOI
10.1145/3473856.3474018
Cited by
25
References
2
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
Volkswagen Foundation (AZ 98513)

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