Benchmarking and Survey of Explanation Methods for Black Box Models

The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions. Retrieving explanations is fundamental to unveil possible biases and to resolve practical or ethical issues. Nowadays, the...

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Bibliographic Details
Main Authors Bodria, Francesco, Giannotti, Fosca, Guidotti, Riccardo, Naretto, Francesca, Pedreschi, Dino, Rinzivillo, Salvatore
Format Journal Article
LanguageEnglish
Published 25.02.2021
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computers and Society
Computer Science - Learning
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Summary:The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions. Retrieving explanations is fundamental to unveil possible biases and to resolve practical or ethical issues. Nowadays, the literature is full of methods with different explanations. We provide a categorization of explanation methods based on the type of explanation returned. We present the most recent and widely used explainers, and we show a visual comparison among explanations and a quantitative benchmarking.
DOI:10.48550/arxiv.2102.13076