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Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations

arXiv 2604.09573 ↗First posted 22 Feb 2026cs.HC cs.LGcited by 0

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Abstract (arXiv)

Experimental user studies evaluating the effectiveness of different subtypes of post-hoc explanations for black-box models are largely nonexistent. Therefore, the aim of this study was to investigate and evaluate how different types of counterfactual explanations, namely single point explanations and interval-based explanations, affect both model understanding and (demonstrated) trust. We conducted an online user study using a within-subjects experimental design, where the experimental arms were (i) no explanation (control), (ii) feature importance scores, (iii) point counterfactual explanations, and (iv) interval counterfactual explanations. Our results clearly show the superiority of interval explanations over other tested explanation types in increasing both model understanding and demonstrated trust in the AI. We could not support findings of some previous studies showing an effect of point counterfactual explanations compared to the control group. Our results further highlight the role individual differences in, for example, cognitive style or personality, in explanation effectiveness.

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References · 21

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  3. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  4. 2018Actionable Recourse in Linear Classificationcited by 327
  5. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  6. 2019Model-Agnostic Counterfactual Explanations for Consequential Decisionscited by 204
  7. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  8. 2019Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifierscited by 143
  9. 2019Efficient Search for Diverse Coherent Explanationscited by 111
  10. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  11. 2020GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learningcited by 50
  12. 2022Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recoursecited by 40
9 more
  1. 2019Why Does My Model Fail? Contrastive Local Explanations for Retail Forecastingcited by 31
  2. 2021Optimization with Constraint Learning: A Framework and Surveycited by 28
  3. 2023Finding Regions of Counterfactual Explanations via Robust Optimizationcited by 19
  4. 2022Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAIcited by 16
  5. 2022Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differcited by 12
  6. 2022Are we measuring trust correctly in explainability, interpretability, and transparency research?cited by 11
  7. 2022Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learningcited by 9
  8. 2022Counterfactual Explanations Using Optimization With Constraint Learningcited by 8
  9. 2025Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methodscited by 3

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