mlsanityData to 7 Oct 2026

Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found Using Counterfactuals As Guides?

arXiv 2403.00980 ↗First posted 1 Mar 2024cs.AIcited by 5

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Abstract (arXiv)

Recently, counterfactuals using "if-only" explanations have become very popular in eXplainable AI (XAI), as they describe which changes to feature-inputs of a black-box AI system result in changes to a (usually negative) decision-outcome. Even more recently, semi-factuals using "even-if" explanations have gained more attention. They elucidate the feature-input changes that do not change the decision-outcome of the AI system, with a potential to suggest more beneficial recourses. Some semi-factual methods use counterfactuals to the query-instance to guide semi-factual production (so-called counterfactual-guided methods), whereas others do not (so-called counterfactual-free methods). In this work, we perform comprehensive tests of 8 semi-factual methods on 7 datasets using 5 key metrics, to determine whether counterfactual guidance is necessary to find the best semi-factuals. The results of these tests suggests not, but rather that computing other aspects of the decision space lead to better semi-factual XAI.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 26 of its 37 references that are in the map. First posted on arXiv 1 Mar 2024.

Builds on

26 of 37 references.

References · 26

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  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. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  5. 2018Actionable Recourse in Linear Classificationcited by 327
  6. 2018On the Robustness of Interpretability Methodscited by 310
  7. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  8. 2018To Trust Or Not To Trust A Classifiercited by 205
  9. 2020Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Reviewcited by 174
  10. 2020Multi-Objective Counterfactual Explanationscited by 157
  11. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  12. 2020Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)cited by 79
14 more
  1. 2021NICE: An Algorithm for Nearest Instance Counterfactual Explanationscited by 62
  2. 2020On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learningcited by 60
  3. 2023Robust Counterfactual Explanations for Neural Networks With Probabilistic Guaranteescited by 45
  4. 2021GeCo: Quality Counterfactual Explanations in Real Timecited by 32
  5. 2021Conditional Generative Models for Counterfactual Explanationscited by 28
  6. 2022A Rationale-Centric Framework for Human-in-the-loop Machine Learningcited by 27
  7. 2023Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAIcited by 20
  8. 2021A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanationscited by 19
  9. 2023Natural Example-Based Explainability: a Surveycited by 13
  10. 2022"Even if ..." -- Diverse Semifactual Explanations of Rejectcited by 11
  11. 2022This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopycited by 8
  12. 2023The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomescited by 8
  13. 2022Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systemscited by 7
  14. 2019Interpreting Neural Networks Using Flip Pointscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 2.

5 papers.

  1. 2024Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factualscited by 1
  2. 2024Semifactual Explanations for Reinforcement Learningcited by 1
  3. 2024iSee: Advancing Multi-Shot Explainable AI Using Case-based Recommendationscited by 0
  4. 2026On the Hardness of Computing Counterfactual and Semifactual Explanations in XAIcited by 0
  5. 2026Informative Semi-Factuals for XAI: The Elaborated Explanations that People Prefercited by 0