mlsanityData to 7 Oct 2026

Are we measuring trust correctly in explainability, interpretability, and transparency research?

arXiv 2209.00651 ↗First posted 31 Aug 2022cs.HC cs.AIcited by 11

Not on the map. Why?

Abstract (arXiv)

This paper presents an argument for why we are not measuring trust sufficiently in explainability, interpretability, and transparency research. Most studies ask participants to complete a trust scale to rate their trust of a model that has been explained/interpreted. If the trust is increased, we consider this a positive. However, there are two issues with this. First, we usually have no way of knowing whether participants should trust the model. Trust should surely decrease if a model is of poor quality. Second, these scales measure perceived trust rather than demonstrated trust. This paper showcases three methods that do a good job at measuring perceived and demonstrated trust. It is intended to be starting point for discussion on this topic, rather than to be the final say. The author invites critique and discussion.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 31 Aug 2022.

Builds on

4 of 28 references.

References · 4

  1. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  2. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  3. 2019Quantifying Interpretability and Trust in Machine Learning Systemscited by 49
  4. 2020Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Mapscited by 30

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 1; 2025: 0; 2026: 2.

11 papers.

  1. 2022"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interactioncited by 63
  2. 2023A Systematic Review on Fostering Appropriate Trust in Human-AI Interactioncited by 21
  3. 2023GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanationscited by 17
  4. 2023Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Applicationcited by 14
  5. 2022Experiential Explanations for Reinforcement Learningcited by 5
  6. 2023Trust and Transparency in Recommender Systemscited by 3
  7. 2023Visualization for Recommendation Explainability: A Survey and New Perspectivescited by 2
  8. 2024Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systemscited by 2
  9. 2023Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Modelcited by 1
  10. 2026Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanationscited by 0
  11. 2026Investigating the Effects of Different Levels of User Control in an Interactive Educational Recommender Systemcited by 0