Agent-Causation, Explanation, and Akrasia: A Reply to Levy’s Hard Luck
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Laugel et al. raise the issue of justification for counterfactual explanation.
“Laugel et al. raise the issue of justification for counterfactual explanation [144].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [144]
In Laugel et al., counterfactuals are selected from a local neighbourhood circling around the test example with the radius of the distance to the closest correctly predicted data point of a contrast-class.
“Counterfactuals are selected from a local neighbourhood circling around the test example with the radius of the distance to the closest correctly predicted data point of a contrast-class.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [144]
Laugel et al. argue that a synthesized counterfactual data point must be connected to the training data.
“They argue that a synthesized counterfactual data point must be connected to the training data.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [144]
The candidate counterfactuals are then clustered, as the initial local neighbourhood is updated to become a more extensive hyperspherical layer, until it can no longer be extended.
“The candidate counterfactuals are then clustered, as the initial local neighbourhood is updated to become a more extensive hyperspherical layer, until it can no longer be extended.”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [144]
Visual input data are explained by contrastive patterns between the given data example and that of an opposing class in one iteration or a series thereof.
“or explained by contrastive patterns between the given data example and that of an opposing class in one iteration [130], [144] or a series thereof [49]”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [144]
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- Authors
- Franklin, C. E.
- Affiliations
- Marymount University (US)
- Corresponding author
- Franklin, Christopher Evan
- Venue
- Criminal Law and Philosophy
- Published
- 2013-11-08
- Volume
- 9
- Issue
- 4
- Pages
- 753-770
- ISSN
- 1871-9791 (ISSN-L)
- 1871-9805
- OpenAlex
- W2020865825
- Cited by
- 7
- References
- 40
- Citations by year
- 2023: 1 · 2021: 2 · 2019: 1 · 2017: 1 · 2014: 2
- Type
- book review
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Criminal Law and Philosophy (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Free Will and Agency
- Social Sciences › Arts and Humanities › Philosophy › Philosophical Ethics and Theory
- Social Sciences › Arts and Humanities › Philosophy › Theology and Philosophy of Evil
- Keywords
- agent causation, akrasia, moral responsibility, contrastive explanations, libertarianism, determinism, indeterminism, self-control, motivation, moral luck
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