Contrastive Explanations for a Deep Learning Model on Time-Series Data
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A large body of research on both causal and non-causal counterfactual explanation testifies that counterfactuals have a diverse nature with respect to causality.
“Indeed, a large body of research on both causal and non-causal counterfactual explanation testifies that counterfactuals have a diverse nature with respect to causality [104].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [104]
The citation lists haven’t been fetched yet
References 16 · Cited by 17
- Authors
- Labaien, J., Zugasti, E., & De Carlos, X.
- Affiliations
- Ikerlan (ES)
- Mondragon Unibertsitatea (ES)
- Corresponding author
- Labaien, Jokin
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 235-244
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3085010542
- Cited by
- 17
- References
- 16
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 2
- FWCI
- 0.57
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Communities in ADDI (University of the Basque Country) (submittedVersion, other-oa)
- Hispana (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- explainable AI, time series classification, interpretability, deep learning models, model-agnostic explanation, time series
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 ›
Are you an author, or do you see a problem with this page? Request a review
