Second chance for a first impression? Trust development in intelligent system interaction

Suzanne Tolmeijer, Ujwal Gadiraju, Ramya Ghantasala, Akshit Gupta, Abraham Bernstein

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

There is a growing use of intelligent systems to support human decision-making across several domains. Trust in intelligent systems, however, is pivotal in shaping their widespread adoption. Little is currently understood about how trust in an intelligent system evolves over time and how it is mediated by the accuracy of the system. We aim to address this knowledge gap by exploring trust formation over time and its relation to system accuracy. To that end, we built an intelligent house recommendation system and carried out a longitudinal study consisting of 201 participants across 3 sessions in a week. In each session, participants were tasked with finding housing that fit a given set of constraints using a conventional web interface that reflected a typical housing search website. Participants could choose to use an intelligent decision support system to help them find the right house. Depending on the group, participants received a variation of accurate or inaccurate advice from the intelligent system throughout each session. We measured trust using a trust in automation scale at the end of each session. We found evidence suggesting that trust development is a slow process that evolves over multiple sessions, and that first impressions of the intelligent system are highly influential. Our results echo earlier research on trust formation in single session interactions, corroborating that reliability, validity, predictability, and dependability all influence trust formation. We also found that the age of the participants and their affinity with technology had an effect on their trust in the intelligent system. Our findings highlight the importance of first impressions and improvement of system accuracy for trust development. Hence, our study is an important first step in understanding trust development, breakdown of trust, and trust repair over multiple system interactions, informing improved system design.

Original languageEnglish
Title of host publicationUMAP 2021 - Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery (ACM)
Pages77-87
Number of pages11
ISBN (Electronic)9781450383660
DOIs
Publication statusPublished - 2021
Event29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021 - Virtual, Online, Netherlands
Duration: 21 Jun 202025 Jun 2020

Publication series

NameUMAP 2021 - Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021
CountryNetherlands
CityVirtual, Online
Period21/06/2025/06/20

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Decision Support
  • Human-AI Interaction
  • Intelligent System
  • Trust development
  • Trust Repair

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