mlsanityData to 7 Oct 2026

The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems

arXiv 2008.09100 ↗First posted 20 Aug 2020cs.HC cs.AI cs.LGcited by 34

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Abstract (arXiv)

Domain-specific intelligent systems are meant to help system users in their decision-making process. Many systems aim to simultaneously support different users with varying levels of domain expertise, but prior domain knowledge can affect user trust and confidence in detecting system errors. While it is also known that user trust can be influenced by first impressions with intelligent systems, our research explores the relationship between ordering bias and domain expertise when encountering errors in intelligent systems. In this paper, we present a controlled user study to explore the role of domain knowledge in establishing trust and susceptibility to the influence of first impressions on user trust. Participants reviewed an explainable image classifier with a constant accuracy and two different orders of observing system errors (observing errors in the beginning of usage vs. in the end). Our findings indicate that encountering errors early-on can cause negative first impressions for domain experts, negatively impacting their trust over the course of interactions. However, encountering correct outputs early helps more knowledgable users to dynamically adjust their trust based on their observations of system performance. In contrast, novice users suffer from over-reliance due to their lack of proper knowledge to detect errors.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2020.

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Citing papers per year: 2020: 1; 2021: 2; 2022: 7; 2023: 10; 2024: 4; 2025: 5; 2026: 5.

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  1. 2022Interactive Model Cards: A Human-Centered Approach to Model Documentationcited by 61
  2. 2024Determinants of LLM-assisted Decision-Makingcited by 55
  3. 2022Improving Expert Predictions with Conformal Predictioncited by 54
  4. 2023Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfactioncited by 32
  5. 2021Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Reviewcited by 30
  6. 2023Human-Aligned Calibration for AI-Assisted Decision Makingcited by 28
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  8. 2023ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactionscited by 22
  9. 2023The Impact of Imperfect XAI on Human-AI Decision-Makingcited by 21
  10. 2020Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracycited by 19
  11. 2023Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Makingcited by 18
  12. 2023Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic reviewcited by 18
22 more
  1. 2024Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Makingcited by 14
  2. 2023Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Makingcited by 11
  3. 2022Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertisecited by 8
  4. 2023Towards an Understanding and Explanation for Mixed-Initiative Artificial Scientific Text Detectioncited by 8
  5. 2023Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advicecited by 8
  6. 2024To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI Systemscited by 7
  7. 2022Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systemscited by 6
  8. 2025Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translationcited by 5
  9. 2022Factors that influence the adoption of human-AI collaboration in clinical decision-makingcited by 4
  10. 2022Painting the black box white: experimental findings from applying XAI to an ECG reading settingcited by 4
  11. 2024What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidencecited by 3
  12. 2021An Interpretable Algorithm for Uveal Melanoma Subtyping from Whole Slide Cytology Imagescited by 1
  13. 2022The Ability of Image-Language Explainable Models to Resemble Domain Expertisecited by 1
  14. 2025Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translationscited by 1
  15. 2026Learning to Decide with AI Assistance under Human-Alignmentcited by 1
  16. 2023Incorporating Experts' Judgment into Machine Learning Modelscited by 0
  17. 2025Evaluation and Incident Prevention in an Enterprise AI Assistantcited by 0
  18. 2025Human Cognitive Biases in Explanation-Based Interaction: The Case of Within and Between Session Order Effectcited by 0
  19. 2026Human-AI Collaboration in Large Language Model-Integrated Building Energy Management Systems: The Role of User Domain Knowledge and AI Literacycited by 0
  20. 2026Human-in-the-Loop User Feedback Affects Perceived Accuracy and Trust, but Task Subjectivity Matterscited by 0
  21. 2026Assistant or Actor? Student Trust, Control, and Delegation Regret When Using a General-Purpose AI Agentcited by 0
  22. 2026How AI Assistance Affects Human Skill Development: A Study of Learning with Logic Puzzlescited by 0