To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods

The main objective of eXplainable Artificial Intelligence (XAI) is to provide effective explanations for black-box classifiers. The existing literature lists many desirable properties for explanations to be useful, but there is a scarce consensus on how to quantitatively evaluate explanations in pra...

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Detalles Bibliográficos
Autores principales: Amparore, Elvio, Perotti, Alan, Bajardi, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056245/
https://www.ncbi.nlm.nih.gov/pubmed/33977131
http://dx.doi.org/10.7717/peerj-cs.479

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056245/
https://www.ncbi.nlm.nih.gov/pubmed/33977131
http://dx.doi.org/10.7717/peerj-cs.479

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