Agent EXPRI: Licence to Explain
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The truth of a counterfactually inferred statement is resolved by modeling a situation where the smallest possible change in features of the actual world leads to a different state of things and estimating what is true in that setting.
“In this setting, the truth of a counterfactually inferred statement is resolved by: (1) modeling a situation where the smallest possible change in features of the actual world (as set in the antecedent) leads to a different (possibly, desired) state of things (the so-called ''closest'' or ''nearest'' possible world); and (2) estimating what is true in that setting [35].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [35]
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References 45 · Cited by 11
Research works printed but not linked: 4
- [13]Ilia, P., Polakis, I., Athanasopoulos, E., Maggi, F., Ioannidis, S.: Face/Off: preventing privacy leakage from photos in social networks. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security - CCS 2015, pp. 781–792. ACM Press, New York (2015)
- [22]Mosca, F., Such, J.M., McBurney, P.: Value-driven collaborative privacy decision making. In: Proceedings of the AAAI Symposium on Privacy-Enhancing Artificial Intelligence and Language Technologies (PAL) (2019)
- [28]Sarkadi, S., McBurney, P., Parsons, S.: Deceptive storytelling in artificial dialogue games. In: Proceedings of the AAAI 2019 Spring Symposium Series on Story-Enabled Intelligence (2019)
- [33]Squicciarini, A.C., Sundareswaran, S., Lin, D., Wede, J.: A3P: adaptive policy prediction for shared images over popular content sharing sites. In: Proceedings of the 22nd ACM conference on Hypertext and hypermedia, pp. 261–270. ACM (2011)
- Authors
- Mosca, F., Sarkadi, Ş., Such, J. M., & McBurney, P.
- Affiliations
- King's College London (GB)
- Corresponding author
- Mosca, Francesca
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 21-38
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC7338150
- OpenAlex
- W3041097544
- Cited by
- 11
- References
- 45
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 1 · 2022: 3 · 2021: 2 · 2020: 1
- FWCI
- 0.67
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed Central (publishedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Keywords
- social networking sites, privacy conflicts, agent architecture, practical reasoning, explainability, contrastive explanations, moral values, content sharing, collaborative decision-making
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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