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

Authors:

Sunnie S. Y. Kim,

Elizabeth Anne Watkins,

Olga Russakovsky,

Andrés Monroy-HernándezAuthors Info & Claims

FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

Pages 77 - 88

https://doi.org/10.1145/3593013.3593978

Published: 12 June 2023 Publication History

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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.

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Cited By

Patel AGu QEsper RMaeser DMaeser N(2024)The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in HealthcareBioMedInformatics10.3390/biomedinformatics40200754:2(1363-1383)Online publication date: 17-May-2024
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Alharbi RLor PHerskovitz JSchoenebeck SBrewer R(2024)Misfitting With AI: How Blind People Verify and Contest AI ErrorsProceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3663548.3675659(1-17)Online publication date: 27-Oct-2024
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Cheng RWang RZimmermann TFord D(2024)“It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI-Powered Code Generation ToolsACM Transactions on Interactive Intelligent Systems10.1145/365199014:2(1-39)Online publication date: 15-May-2024
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Index Terms

Humans, AI, and Context: Understanding End-Users’ Trust in a Real-World Computer Vision Application
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. HCI design and evaluation methods
      1. User studies

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FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

June 2023

1929 pages

ISBN:9798400701924

DOI:10.1145/3593013

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Princeton SEAS Howard B. Wentz, Jr. Junior Faculty Award
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FAccT '23: the 2023 ACM Conference on Fairness, Accountability, and Transparency

June 12 - 15, 2023

IL, Chicago, USA

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https://dl.acm.org/doi/10.1145/3651990
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Wang RCheng RFord DZimmermann T(2024)Investigating and Designing for Trust in AI-powered Code Generation ToolsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658984(1475-1493)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658984
Manzini AKeeling GMarchal NMcKee KRieser VGabriel I(2024)Should Users Trust Advanced AI Assistants? Justified Trust As a Function of Competence and AlignmentProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658964(1174-1186)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658964
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https://dl.acm.org/doi/10.1145/3630106.3658941
Degachi CMehrotra SYurrita MNiforatos ETielman M(2024)Practising Appropriate Trust in Human-Centred AI DesignExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650825(1-8)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650825
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