mlsanityData to 7 Oct 2026

Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals

arXiv 2303.09297 ↗First posted 16 Mar 2023cs.AI cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

Counterfactual explanations are an increasingly popular form of post hoc explanation due to their (i) applicability across problem domains, (ii) proposed legal compliance (e.g., with GDPR), and (iii) reliance on the contrastive nature of human explanation. Although counterfactual explanations are normally used to explain individual predictive-instances, we explore a novel use case in which groups of similar instances are explained in a collective fashion using ``group counterfactuals'' (e.g., to highlight a repeating pattern of illness in a group of patients). These group counterfactuals meet a human preference for coherent, broad explanations covering multiple events/instances. A novel, group-counterfactual algorithm is proposed to generate high-coverage explanations that are faithful to the to-be-explained model. This explanation strategy is also evaluated in a large, controlled user study (N=207), using objective (i.e., accuracy) and subjective (i.e., confidence, explanation satisfaction, and trust) psychological measures. The results show that group counterfactuals elicit modest but definite improvements in people's understanding of an AI system. The implications of these findings for counterfactual methods and for XAI are discussed.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 37 of its 66 references that are in the map. First posted on arXiv 16 Mar 2023.

Builds on

37 of 66 references.

References · 37

  1. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  2. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  3. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  4. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  5. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  6. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  7. 2019Counterfactual Visual Explanationscited by 312
  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. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  11. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 232
  12. 2018Local Rule-Based Explanations of Black Box Decision Systemscited by 165
25 more
  1. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  2. 2020Multi-Objective Counterfactual Explanationscited by 157
  3. 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 139
  4. 2019Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systemscited by 128
  5. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
  6. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  7. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  8. 2020Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)cited by 79
  9. 2019An Evaluation of the Human-Interpretability of Explanationcited by 71
  10. 2020One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparencycited by 64
  11. 2020Instance-based Counterfactual Explanations for Time Series Classificationcited by 64
  12. 2021NICE: An Algorithm for Nearest Instance Counterfactual Explanationscited by 62
  13. 2020On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learningcited by 60
  14. 2021The Use and Misuse of Counterfactuals in Ethical Machine Learningcited by 52
  15. 2018Interpretable Credit Application Predictions With Counterfactual Explanationscited by 43
  16. 2022Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AIcited by 40
  17. 2019Why Does My Model Fail? Contrastive Local Explanations for Retail Forecastingcited by 31
  18. 2020Explaining Groups of Points in Low-Dimensional Representationscited by 16
  19. 2022Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAIcited by 16
  20. 2022Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Settingcited by 11
  21. 2022Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learningcited by 9
  22. 2022One Explanation to Rule them All -- Ensemble Consistent Explanationscited by 7
  23. 2023Users are the North Star for AI Transparencycited by 5
  24. 2022Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidencecited by 4
  25. 2021Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cowscited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 8.

9 papers.

  1. 2024AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systemscited by 7
  2. 2024Counterfactual Metarules for Local and Global Recoursecited by 7
  3. 2024Distributional Counterfactual Explanations With Optimal Transportcited by 6
  4. 2024GLANCE: Global Actions in a Nutshell for Counterfactual Explainabilitycited by 6
  5. 2024A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanationscited by 5
  6. 2023Reason to explain: Interactive contrastive explanations (REASONX)cited by 2
  7. 2024Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamicscited by 1
  8. 2024One-for-many Counterfactual Explanations by Column Generationcited by 1
  9. 2024Explainable bank failure prediction models: Counterfactual explanations to reduce the failure riskcited by 1