The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
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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“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]
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“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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Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
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Explainable artificial intelligence: an analytical review
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Metadata
- 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 of Biology and Scientific Explanation
- Keywords
- counterfactuals, explainable AI, human reasoning, counterfactual reasoning, interpretability, model interpretability
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