City Research Online

Abstract

Trust is a significant factor in user adoption of new systems. However, although trust is a dynamic attitude of the user towards the system and changes over time, trust in intelligent systems is typically captured as a single quantitative measure at the conclusion of a task. This paper challenges this approach. We report a case study that employed a combination of repeated quantitative and qualitative measures to examine how trust in an intelligent system evolved over time and whether this varied depending on whether the system offered explanations. We discovered different patterns in participants' trust journeys. When provided with explanations, participants' trust levels initially increased, before returning to their original level. Without explanations, participants' trust reduced over time. The qualitative data showed that perceived system ability was more important in determining trust amongst with explanation participants and perceived transparency was a greater influence on the trust of participants who did not receive explanations. The findings provide a deeper understanding of the development of user trust in intelligent systems and indicate the value of the approach adopted.

Publication Type:	Book Section
Additional Information:	© Stumpf, S. | ACM 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, http://dx.doi.org/10.1145/2858036.2858533
Publisher Keywords:	Trust; Intelligent Systems; Qualitative Evaluations; Quantitative Evaluations; Case Study
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science > Human Computer Interaction Design

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