mlsanityData to 7 Oct 2026

Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence

arXiv 2206.02790 ↗First posted 6 Jun 2022cs.LG cs.AI cs.HCcited by 4

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Abstract (arXiv)

In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies. Showing confidence scores in human-agent interaction systems can help build trust between humans and AI systems. However, most existing research only used the confidence score as a form of communication, and we still lack ways to explain why the algorithm is confident. This paper also presents two methods for understanding model confidence using counterfactual explanation: (1) based on counterfactual examples; and (2) based on visualisation of the counterfactual space.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 6 Jun 2022.

Builds on

19 of 39 references.

References · 19

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  4. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  5. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  6. 2019Counterfactual Visual Explanationscited by 312
  7. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  8. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 261
  9. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  10. 2020Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertaintycited by 128
  11. 2019Efficient Search for Diverse Coherent Explanationscited by 111
  12. 2020Getting a CLUE: A Method for Explaining Uncertainty Estimatescited by 92
7 more
  1. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  2. 2020Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)cited by 79
  3. 2021Contrastive Explanations for Model Interpretabilitycited by 75
  4. 2018Contrastive Explanation: A Structural-Model Approachcited by 64
  5. 2020Instance-based Counterfactual Explanations for Time Series Classificationcited by 64
  6. 2021Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutionscited by 16
  7. 2021Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanationscited by 15

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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactualscited by 9
  2. 2024Explainable and Human-Grounded AI for Decision Support Systems: The Theory of Epistemic Quasi-Partnershipscited by 3
  3. 2023T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systemscited by 1
  4. 2025Finding Uncommon Ground: A Human-Centered Model for Extrospective Explanationscited by 0