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DESIGNING FOR CALIBRATED TRUST: EXPLORING THE CHALLENGES IN CALIBRATING TRUST BETWEEN USERS AND AUTONOMOUS VEHICLES

Published online by Cambridge University Press:  27 July 2021

David Callisto Valentine ,
Iskander Smit  and
Euiyoung Kim
David Callisto Valentine*
Affiliation:
Delft University of Technology
Iskander Smit
Affiliation:
Delft University of Technology
Euiyoung Kim
Affiliation:
Delft University of Technology
*
Valentine, David Callisto, Delft University of Technology, Industrial Design Engineering, Netherlands, The, david.cal.valentine@gmail.com

Abstract

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Trust is an important factor in building acceptance of autonomous vehicles within our society, but the complex nature of trust makes it challenging to design for an appropriate level of trust. This can lead to instances of mistrust and/or distrust between users and AV’s. Designing for calibrated trust is a possible option to address this challenge. Existing research on designing for calibrated trust focuses on the human machine interaction (HMI), while from literature we infer that trust creation beings much before the first interaction between a user and an AV. The goal of our research is to broaden the scope of calibrated trust, by exploring the pre-use phase and understand the challenges faced in calibration of trust. Within our study 16 mobility experts were interviewed and a thematic analysis of the interviews was conducted. The analysis revealed the lack of clear communication between stakeholders, a solutionism approach towards designing and lack of transparency in design as the prominent challenges. Building on the research insights, we briefly introduce the Calibrated Trust Toolkit as our design solution, and conclude by proposing a sweet spot for achieving calibration of trust between users and autonomous vehicles.

Keywords

CommunicationDesign to XArtificial intelligenceCalibrated TrustAI Solutionism
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1143 - 1152
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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