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On generating trustworthy counterfactual explanations
Open accessAbstract
Deep learning models like chatGPT exemplify AI success but necessitate a deeper understanding of trust in critical sectors. Trust can be achieved using counterfactual explanations, which is how humans become familiar with unknown processes; by understanding the hypothetical input circumstances under which the output changes. We argue that the generation of counterfactual explanations requires several aspects of the generated counterfactual instances, not just their counterfactual ability. We present a framework for generating counterfactual explanations that formulate its goal as a multiobjective optimization problem balancing three objectives: plausibility; the intensity of changes; and adversarial power. We use a generative adversarial network to model the distribution of the input, along with a multiobjective counterfactual discovery solver balancing these objectives. We demonstrate the usefulness of six classification tasks with image and 3D data confirming with evidence the existence of a trade-off between the objectives, the consistency of the produced counterfactual explanations with human knowledge, and the capability of the framework to unveil the existence of concept-based biases and misrepresented attributes in the input domain of the audited model. Our pioneering effort shall inspire further work on the generation of plausible counterfactual explanations in real-world scenarios where attribute-/concept-based annotations are available for the domain under analysis.
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“The study demonstrates across six classification tasks using image and 3D data confirm evidence of trade-offs between objectives and consistency of the generated explanations with human understanding, and the framework's ability to reveal concept-based biases and misrepresented attributes within the model's input domain.”
From Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human (2025) · cites this paper as [34]
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Metadata
- Authors
- Del Ser, J., Barredo-Arrieta, A., Díaz-Rodríguez, N., Herrera, F., Saranti, A., & Holzinger, A.
- Affiliations
- Universidad de Granada (ES)
- Medical University of Graz (AT)
- Corresponding author
- Holzinger, Andreas
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 655
- Pages
- 119898
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W4388767362
- Cited by
- 122
- References
- 39
- Citations by year
- 2026: 22 · 2025: 49 · 2024: 51
- FWCI
- 11.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information Sciences (publishedVersion, cc-by)
- Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- Funders
- European Commission (CER-20211030)
- Horizon 2020 (826078)
- Austrian Science Fund (P-32554)
- H2020 Marie Skłodowska-Curie Actions (101059332)
- Centro para el Desarrollo Tecnológico Industrial
- Eusko Jaurlaritza (IT1256-22)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- counterfactual explanations, trustworthy AI, multi-objective optimization, generative adversarial networks, plausibility, explainable deep learning, image classification
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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