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Defense Against Explanation Manipulation

Explainable machine learning attracts increasing attention as it improves the transparency of models, which is helpful for machine learning to be trusted in real applications. However, explanation methods have recently been demonstrated to be vulnerable to manipulation, where we can easily change a...

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Detalles Bibliográficos
Autores principales: Tang, Ruixiang, Liu, Ninghao, Yang, Fan, Zou, Na, Hu, Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866947/
https://www.ncbi.nlm.nih.gov/pubmed/35224483
http://dx.doi.org/10.3389/fdata.2022.704203

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