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Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directions.
- Source :
- Enterprise Information Systems; Sep2023, Vol. 17 Issue 9, p1-70, 70p
- Publication Year :
- 2023
-
Abstract
- Explainable artificial intelligence (XAI) is an evolving discipline that mainly emphasises unboxing in these Black-Boxes. This study provides in-depth review of XAI literature together with a new taxonomy of categorising XAI methods. Moreover, the security of Deep learning (DL) against different attacks turned to be a critical concern for both industry and academia. This study presents a taxonomic overview of the attacks on DL solutions and methods for securing DL against these attacks. Experiments are performed to evaluate and analyse the cutting-edge methods for explaining and securing DL models on real-world case studies of Twitter sentimental analysis using state-of-the-art DL models. [ABSTRACT FROM AUTHOR]
- Subjects :
- ARTIFICIAL intelligence
DEEP learning
TAXONOMY
MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 17517575
- Volume :
- 17
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- Enterprise Information Systems
- Publication Type :
- Academic Journal
- Accession number :
- 168582646
- Full Text :
- https://doi.org/10.1080/17517575.2022.2098537