mlsanityData to 7 Oct 2026

Trust and Reliance in XAI -- Distinguishing Between Attitudinal and Behavioral Measures

arXiv 2203.12318 ↗First posted 23 Mar 2022cs.HC cs.AIcited by 10

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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.

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10 of 43 references.

References · 10

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2016The Mythos of Model Interpretabilitycited by 716
  3. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  4. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  5. 2018Manipulating and Measuring Model Interpretabilitycited by 256
  6. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
  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. 2019Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptionscited by 81

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Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 1.

10 papers.