Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning
Open accessAbstract
Counterfactuals about what could have happened are increasingly used in an array of Artificial Intelligence (AI) applications, and especially in explainable AI (XAI). Counterfactuals can aid the provision of interpretable models to make the decisions of inscrutable systems intelligible to developers and users. However, not all counterfactuals are equally helpful in assisting human comprehension. Discoveries about the nature of the counterfactuals that humans create are a helpful guide to maximize the effectiveness of counterfactual use in AI.
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The use of counterfactual explanations can help the user to understand the decision of a model.
“It was also shown that the use of counterfactual explanations can help the user to understand the decision of a model [40, 42, 351].”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [351]
Fang et al. referred to contrastive reasoning as a procedure operating on but-statements.
“Alternatively, Fang et al. [43] referred to contrastive reasoning as a procedure operating on ''butstatements'', as in ''all cars are polluting, but hybrid cars are not polluting'', which serves a principally different explanation generation task in comparison with the other aforementioned approaches.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [43]
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Explainable AI and Causal Understanding: Counterfactual Approaches Considered
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How AI tools can—and cannot—help organizations become more ethical
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Getting a CLUE: A Method for Explaining Uncertainty Estimates
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Explaining Any Time Series Classifier
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Evaluating Robustness of Counterfactual Explanations
Artelt, A., Vaquet, V., Velioğlu, R., Hinder, F., Brinkrolf, J., Schilling, M., & Hammer, B. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9660058 · Cited by 43 · see citing works
Counterfactual Explanations for Prediction and Diagnosis in XAI
Dai, X., Keane, M. T., Shalloo, L., Ruelle, E., & Byrne, R. M. J. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534144 · Cited by 43 · see citing works
Self-citationEbermann, C., Selisky, M., & Weibelzahl, S. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2126812 · Cited by 43 · see citing works
Directive Explanations for Actionable Explainability in Machine Learning Applications
Singh, R., Miller, T., Lyons, H., Sonenberg, L., Velloso, E., Vetere, F., Howe, P. D. L., & Dourish, P. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3579363 · Cited by 50 · see citing works
Principles of Explainable Artificial Intelligence
Guidotti, R., Monreale, A., Pedreschi, D., & Giannotti, F. (2021) · Springer eBooks · doi:10.1007/978-3-030-76409-8_2 · Cited by 40 · see citing works
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
Yang, L., Li, J., Cunningham, P., Zhang, Y., Smyth, B., & Dong, R. (2021) · doi:10.18653/v1/2021.acl-long.26 · Cited by 43 · see citing works
Explainable software systems: from requirements analysis to system evaluation
Chazette, L., Brunotte, W., & Speith, T. (2022) · Requirements Engineering · doi:10.1007/s00766-022-00393-5 · Cited by 44 · see citing works
Unsupervised Concept Drift Detection From Deep Learning Representations in Real-Time
Greco, S., Vacchetti, B., Apiletti, D., & Cerquitelli, T. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3593123 · Cited by 49 · see citing works
Celar, L., & Byrne, R. M. J. (2023) · Memory & Cognition · doi:10.3758/s13421-023-01407-5 · Cited by 38 · see citing works
Self-citationA historical perspective of biomedical explainable AI research
Malinverno, L., Barros, V., Ghisoni, F., Visonà, G., Kern, R., Nickel, P. J., Ventura, B. E., Šimić, I., Stryeck, S., Manni, F., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100830 · Cited by 42 · see citing works
GeoDTR+: Toward Generic Cross-View Geolocalization via Geometric Disentanglement
Zhang, X., Li, X., Sultani, W., Chen, C., & Wshah, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3443652 · Cited by 35 · see citing works
Natural Example-Based Explainability: A Survey
Poché, A., Hervier, L., & Bakkay, M.-C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44067-0_2 · Cited by 33 · see citing works
Machine learning and structural econometrics: contrasts and synergies
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Feature-Weighted Counterfactual-Based Explanation for Bankruptcy Prediction
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Mikołajczyk, A., Grochowski, M., & Kwasigroch, A. (2020) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2021-0004 · Cited by 32 · see citing works
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
Oliveira, R., & Martens, D. (2021) · Applied Sciences · doi:10.3390/app11167274 · Cited by 34 · see citing works
Smyth, B., & Keane, M. T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14923-8_2 · Cited by 30 · see citing works
Categorical and Continuous Features in Counterfactual Explanations of AI Systems
Warren, G., Byrne, R. M. J., & Keane, M. T. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584090 · Cited by 29 · see citing works
Self-citationArtificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo
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Counterfactual Explanations for Predictive Business Process Monitoring
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Counterfactual explanation generation with minimal feature boundary
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Improving Visualization Interpretation Using Counterfactuals
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Counterfactual explanations as interventions in latent space
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Counterfactual explanations for misclassified images: How human and machine explanations differ
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Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box
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Formalising the Robustness of Counterfactual Explanations for Neural Networks
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Generating Realistic Natural Language Counterfactuals
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Incremental XAI: Memorable Understanding of AI with Incremental Explanations
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Time Series Model Attribution Visualizations as Explanations
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Introducing contextual transparency for automated decision systems
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Understanding Any Time Series Classifier with a Subsequence-based Explainer
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Explainability Pitfalls: Beyond Dark Patterns in Explainable AI
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- Authors
- Byrne, R. M. J.
- Affiliations
- Trinity College Dublin (IE)
- Corresponding author
- Byrne, Ruth M. J.
- ORCID
- Byrne, Ruth M. J.
- Venue
- Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
- Published
- 2019-08
- Pages
- 6276-6282
- OpenAlex
- W2966362896
- Cited by
- 285
- References
- 41
- Citations by year
- 2026: 13 · 2025: 33 · 2024: 38 · 2023: 66 · 2022: 54 · 2021: 52 · 2020: 25 · 2019: 4
- FWCI
- 12.91
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- counterfactuals, explainable AI, human reasoning, counterfactual reasoning, interpretability, model interpretability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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