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Benchmarking and Survey of Explanation Methods for Black Box Models

2021-02-25 18:50:29
Francesco Bodria, Fosca Giannotti, Riccardo Guidotti, Francesca Naretto, Dino Pedreschi, Salvatore Rinzivillo
arXiv_AI
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2102.13076

PDF

https://arxiv.org/pdf/2102.13076.pdf

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