Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application

Sunnie S.Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Trust is an important factor in people's interactions with AI systems. However, there is a lack of empirical studies examining how real end-users trust or distrust the AI system they interact with. Most research investigates one aspect of trust in lab settings with hypothetical end-users. In this paper, we provide a holistic and nuanced understanding of trust in AI through a qualitative case study of a real-world computer vision application. We report findings from interviews with 20 end-users of a popular, AI-based bird identification app where we inquired about their trust in the app from many angles. We find participants perceived the app as trustworthy and trusted it, but selectively accepted app outputs after engaging in verification behaviors, and decided against app adoption in certain high-stakes scenarios. We also find domain knowledge and context are important factors for trust-related assessment and decision-making. We discuss the implications of our findings and provide recommendations for future research on trust in AI.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023
PublisherAssociation for Computing Machinery
Pages77-88
Number of pages12
ISBN (Electronic)9781450372527
DOIs
StatePublished - Jun 12 2023
Event6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023 - Chicago, United States
Duration: Jun 12 2023Jun 15 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023
Country/TerritoryUnited States
CityChicago
Period6/12/236/15/23

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Keywords

  • Case Study
  • Computer Vision
  • Human-AI Interaction
  • Trust in AI

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