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Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations

Fuente: arXiv

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Autori principali:	Sokol, Kacper, Thies, Santo M. A. R., Hüllermeier, Eyke
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
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