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

arXiv: https://arxiv.org/abs/2203.12318 · HTML: https://mlsanity.com/paper/2203.12318/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems](https://mlsanity.com/paper/2001.08298/) (2020)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)

## Built on by (most cited first, 10 of 10)

- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- ["Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction](https://mlsanity.com/paper/2210.03735/) (2022)
- [Determinants of LLM-assisted Decision-Making](https://mlsanity.com/paper/2402.17385/) (2024)
- [How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency maps](https://mlsanity.com/paper/2404.16042/) (2024)
- [Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study](https://mlsanity.com/paper/2305.18307/) (2023)
- [Selectively Providing Reliance Calibration Cues With Reliance Prediction](https://mlsanity.com/paper/2302.09995/) (2023)
- [How Performance Pressure Influences AI-Assisted Decision Making](https://mlsanity.com/paper/2410.16560/) (2024)
- [Hide or Highlight: Understanding the Impact of Factuality Expression on User Trust](https://mlsanity.com/paper/2508.07095/) (2025)
- [Distrust in (X)AI -- Measurement Artifact or Distinct Construct?](https://mlsanity.com/paper/2303.16495/) (2023)
- [Towards a Novel Measure of User Trust in XAI Systems](https://mlsanity.com/paper/2405.05766/) (2024)

## 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.