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Artificial Intelligence Review
Published in: Artificial Intelligence Review 5/2022

18-11-2021

Explainable artificial intelligence: a comprehensive review

Authors: Dang Minh, H. Xiang Wang, Y. Fen Li, Tan N. Nguyen

Published in: Artificial Intelligence Review | Issue 5/2022

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Abstract

Thanks to the exponential growth in computing power and vast amounts of data, artificial intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be ubiquitously adopted in our daily lives. Even though AI-powered systems have brought competitive advantages, the black-box nature makes them lack transparency and prevents them from explaining their decisions. This issue has motivated the introduction of explainable artificial intelligence (XAI), which promotes AI algorithms that can show their internal process and explain how they made decisions. The number of XAI research has increased significantly in recent years, but there lacks a unified and comprehensive review of the latest XAI progress. This review aims to bridge the gap by discovering the critical perspectives of the rapidly growing body of research associated with XAI. After offering the readers a solid XAI background, we analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-modeling explainability. We also pay attention to the current methods that dedicate to interpret and analyze deep learning methods. In addition, we systematically discuss various XAI challenges, such as the trade-off between the performance and the explainability, evaluation methods, security, and policy. Finally, we show the standard approaches that are leveraged to deal with the mentioned challenges.
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Metadata
Title
Explainable artificial intelligence: a comprehensive review
Authors
Dang Minh
H. Xiang Wang
Y. Fen Li
Tan N. Nguyen
Publication date
18-11-2021
Publisher
Springer Netherlands
Published in
Artificial Intelligence Review / Issue 5/2022
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-021-10088-y

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