mlsanityData to 7 Oct 2026

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

arXiv 2305.08598 ↗First posted 15 May 2023cs.HC cs.AIcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 25 of its 64 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

25 of 64 references.

References · 25

  1. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  2. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  3. 2016Top-down Neural Attention by Excitation Backpropcited by 318
  4. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  5. 2018Manipulating and Measuring Model Interpretabilitycited by 256
  6. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 234
  7. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  8. 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
  9. 2020Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systemscited by 138
  10. 2021Are Convolutional Neural Networks or Transformers more like human vision?cited by 109
  11. 2021HIVE: Evaluating the Human Interpretability of Visual Explanationscited by 105
  12. 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 102
13 more
  1. 2021The effectiveness of feature attribution methods and its correlation with automatic evaluation scorescited by 87
  2. 2019The relationship between trust in AI and trustworthy machine learning technologiescited by 78
  3. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  4. 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
  5. 2019"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learningcited by 39
  6. 2022Visual correspondence-based explanations improve AI robustness and human-AI team accuracycited by 37
  7. 2020Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systemscited by 36
  8. 2019Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgmentcited by 31
  9. 2021The Sanction of Authority: Promoting Public Trust in AIcited by 19
  10. 2022Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methodscited by 13
  11. 2022German AI Start-Ups and AI Ethics: Using A Social Practice Lens for Assessing and Implementing Socio-Technical Innovationcited by 11
  12. 2022Are we measuring trust correctly in explainability, interpretability, and transparency research?cited by 11
  13. 2022The Value of Measuring Trust in AI - A Socio-Technical System Perspectivecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 7; 2026: 1.

14 papers.

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  1. 2024"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trustcited by 67
  2. 2024(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advicecited by 61
  3. 2025Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistenciescited by 39
  4. 2024One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generationscited by 13
  5. 2025Malicious LLM-Based Conversational AI Makes Users Reveal Personal Informationcited by 10
  6. 2024Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributionscited by 9
  7. 2025Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articlescited by 6
  8. 2024An Empirical Exploration of Trust Dynamics in LLM Supply Chainscited by 5
  9. 2024Fairness in Autonomous Driving: Towards Understanding Confounding Factors in Object Detection under Challenging Weathercited by 2
  10. 2025Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanationscited by 1
  11. 2025Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicatorscited by 0
  12. 2025Onto-Epistemological Analysis of AI Explanationscited by 0
2 more
  1. 2025In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussionscited by 0
  2. 2026The Global-Local loop: what is missing in bridging the gap between geospatial data from numerous communities?cited by 0