Trust and Reliance in XAI -- Distinguishing Between Attitudinal and Behavioral Measures
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Abstract (arXiv)
Trust is often cited as an essential criterion for the effective use and real-world deployment of AI. Researchers argue that AI should be more transparent to increase trust, making transparency one of the main goals of XAI. Nevertheless, empirical research on this topic is inconclusive regarding the effect of transparency on trust. An explanation for this ambiguity could be that trust is operationalized differently within XAI. In this position paper, we advocate for a clear distinction between behavioral (objective) measures of reliance and attitudinal (subjective) measures of trust. However, researchers sometimes appear to use behavioral measures when intending to capture trust, although attitudinal measures would be more appropriate. Based on past research, we emphasize that there are sound theoretical reasons to keep trust and reliance separate. Properly distinguishing these two concepts provides a more comprehensive understanding of how transparency affects trust and reliance, benefiting future XAI research.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 10 of its 43 references that are in the map. First posted on arXiv 23 Mar 2022.
Builds on
10 of 43 references.
References · 10
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2016The Mythos of Model Interpretabilitycited by 716
- 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
- 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
- 2018Manipulating and Measuring Model Interpretabilitycited by 256
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
- 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
- 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
- 2020Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systemscited by 138
- 2019Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptionscited by 81
Built on by
Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 1.
10 papers.
- 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 80
- 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
- 2024Determinants of LLM-assisted Decision-Makingcited by 55
- 2024How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency mapscited by 6
- 2023Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Studycited by 5
- 2023Selectively Providing Reliance Calibration Cues With Reliance Predictioncited by 1
- 2024How Performance Pressure Influences AI-Assisted Decision Makingcited by 1
- 2025Hide or Highlight: Understanding the Impact of Factuality Expression on User Trustcited by 1
- 2023Distrust in (X)AI -- Measurement Artifact or Distinct Construct?cited by 0
- 2024Towards a Novel Measure of User Trust in XAI Systemscited by 0
- 2025Hide or Highlight: Understanding the Impact of Factuality Expression on User Trust9 Aug 2025
- 2024How Performance Pressure Influences AI-Assisted Decision Making21 Oct 2024
- 2024Towards a Novel Measure of User Trust in XAI Systems9 May 2024
- 2024How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency maps3 Apr 2024
- 2024Determinants of LLM-assisted Decision-Making27 Feb 2024
- 2023Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study15 May 2023
- 2023Distrust in (X)AI -- Measurement Artifact or Distinct Construct?29 Mar 2023
- 2023Selectively Providing Reliance Calibration Cues With Reliance Prediction20 Feb 2023
- 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations4 Feb 2023
- 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction2 Oct 2022