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Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications
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“Additionally, by exposing the system to contradictory examples where the same symptoms can indicate different diseases, the system can learn to evaluate multiple possibilities and make a more accurate diagnosis [91].”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [91]
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Metadata
- Authors
- Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J.
- Affiliations
- Queensland University of Technology (AU)
- Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT)
- Corresponding author
- Moreira, Catarina
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 81
- Pages
- 59-83
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W3214404698
- Cited by
- 302
- References
- 163
- Citations by year
- 2026: 54 · 2025: 75 · 2024: 71 · 2023: 63 · 2022: 32
- FWCI
- 14.57
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Information Fusion (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by-nc-nd)
- OPUS - Open Publications of UTS Scholars (University of Technology Sydney) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Fundação para a Ciência e a Tecnologia (UIDB/50021/2020)
- Queensland University of Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- counterfactual explanations, causality, explainable AI, model-agnostic explainability, deep learning interpretability, spurious correlations
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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