University of Leicester
Browse
1-s2.0-S0925231222012218-main.pdf (3.02 MB)

Explaining deep neural networks: A survey on the global interpretation methods

Download (3.02 MB)
journal contribution
posted on 2024-01-24, 16:25 authored by Rabia Saleem, Bo Yuan, Fatih Kurugollu, Ashiq Anjum, Lu Liu

A substantial amount of research has been carried out in Explainable Artificial Intelligence (XAI) models, especially in those which explain the deep architectures of neural networks. A number of XAI approaches have been proposed to achieve trust in Artificial Intelligence (AI) models as well as provide explainability of specific decisions made within these models. Among these approaches, global interpretation methods have emerged as the prominent methods of explainability because they have the strength to explain every feature and the structure of the model. This survey attempts to provide a comprehensive review of global interpretation methods that completely explain the behaviour of the AI models. We present a taxonomy of the available global interpretations models and systematically highlight the critical features and algorithms that differentiate them from local as well as hybrid models of explainability. Through examples and case studies from the literature, we evaluate the strengths and weaknesses of the global interpretation models and assess challenges when these methods are put into practice. We conclude the paper by providing the future directions of research in how the existing challenges in global interpretation methods could be addressed and what values and opportunities could be realized by the resolution of these challenges.

History

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

  • VoR (Version of Record)

Published in

Neurocomputing

Volume

513

Pagination

165 - 180

Publisher

ELSEVIER

issn

0925-2312

eissn

1872-8286

Copyright date

2022

Available date

2024-01-24

Language

English