Unjustified Classification Regions and Counterfactual Explanations in Machine Learning
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Causal contrastive explanation is frequently found to be designed as an answer to a why-question of the canonical form 'Why P rather than Q?', where P is an explanandum and Q is a foil.
“Causal contrastive explanation is frequently found to be designed as an answer to a why-question of the following canonical form: ''Why P rather than Q?'' where P is an explanandum (i.e., the fact to be explained), Q being a foil (i.e., one of alternative nonoccurring options) [21].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [21]
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References 30 · Cited by 28
- Authors
- Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., & Detyniecki, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- AXA (France) (FR)
- Corresponding author
- Laugel, Thibault
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Volume
- 11907
- Issue
- II
- Pages
- 37-54
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3023774688
- Cited by
- 28
- References
- 30
- Citations by year
- 2026: 1 · 2024: 5 · 2023: 2 · 2022: 8 · 2021: 9 · 2020: 3
- FWCI
- 2.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- counterfactual explanations, post-hoc explanation, classifier robustness, local explanations, black-box models
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.
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