A theory of contrastive causal explanation and its implications concerning the explanatoriness of deterministic and probabilistic hypotheses
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The generated contrastive explanations are attributed to a specific group of users by means of ontology engineering at the cognitive level of the framework to make the explanations adaptive.
“Subsequently, the generated contrastive explanations are attributed to a specific group of users by means of ontology engineering at the cognitive level of the framework to make the explanations adaptive.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [150]
van der Waa et al.'s Foil-Trees framework provides the methodological basis for perceptual-level contrastive explanation generation within the Perceptual-Cognitive Explanation framework.
“Furthermore, their ''Foil-Trees'' framework provides the methodological basis for perceptual-level contrastive explanation generation within the ''Perceptual-Cognitive Explanation'' framework [150].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [150]
Visual-linguistic counterfactual explanations allow for dialogic interaction.
“and allow for dialogic interaction [150].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [150]
An increasing number of interactive frameworks attempt not only to present the automatically generated explanations to the end-user but also interact with him or her.
“However, we find an increasing number of interactive frameworks that attempt not only to present the automatically generated explanations to the end-user but also interact with him or her [46], [150], [155].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [150]
Some publications stress that human evaluation should be encouraged to estimate the quality and effectiveness of the generated counterfactuals.
“However, whereas certain publications do not provide any specific evaluation method, some do stress that human evaluation should be encouraged to estimate the quality and effectiveness of the generated counterfactuals [46], [150], [153].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [150]
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Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376 · see citing works
“Thus, Sober stresses that the question of whether some hypothesis H explains why a non-contrastive proposition E is true is ‘‘incomplete until H is contrasted with an alternative hypothesis’’ [119].”
Background
- Authors
- Sober, E.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Sober, Elliott
- ORCID
- Sober, Elliott
- Venue
- European Journal for Philosophy of Science
- Published
- 2020-07-25
- Volume
- 10
- Issue
- 3
- ISSN
- 1879-4912 (ISSN-L)
- 1879-4920
- OpenAlex
- W3044550187
- Cited by
- 1
- References
- 65
- Citations by year
- 2021: 1
- FWCI
- 0.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal for Philosophy of Science (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy, Science, and History
- Social Sciences › Arts and Humanities › Philosophy › Epistemology, Ethics, and Metaphysics
- Keywords
- probabilistic explanation, low-probability events
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