From Black Box to Glass Box: A Practical Review of Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI) has become essential as machine learning systems are deployed in high-stakes domains such as security, finance, and healthcare. Traditional models often act as “black boxes”, limiting trust and accountability. Traditional models often act as “black boxes”, l...

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Bibliographic Details
Published in:AI (Basel) Vol. 6; no. 11; p. 285
Main Authors: Liu, Xiaoming, Huang, Danni, Yao, Jingyu, Dong, Jing, Song, Litong, Wang, Hui, Yao, Chao, Chu, Weishen
Format: Journal Article Book Review
Language:English
Published: Basel MDPI AG 01.11.2025
Subjects:
Accuracy
Algorithms
Artificial intelligence
Black boxes
Capital investments
Constraining
Decision making
Decision trees
Disclosure
Documentation
explainability
explainable AI
Explainable artificial intelligence
Machine learning
Neural networks
Privacy
Security
security and privacy
Trends
Trustworthiness
trustworthy AI
trustworthy machine learning
Zero Trust architecture
ISSN:2673-2688, 2673-2688
Online Access:Get full text
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Summary:Explainable Artificial Intelligence (XAI) has become essential as machine learning systems are deployed in high-stakes domains such as security, finance, and healthcare. Traditional models often act as “black boxes”, limiting trust and accountability. Traditional models often act as “black boxes”, limiting trust and accountability. However, most existing reviews treat explainability either as a technical problem or a philosophical issue, without connecting interpretability techniques to their real-world implications for security, privacy, and governance. This review fills that gap by integrating theoretical foundations with practical applications and societal perspectives. define transparency and interpretability as core concepts and introduce new economics-inspired notions of marginal transparency and marginal interpretability to highlight diminishing returns in disclosure and explanation. Methodologically, we examine model-agnostic approaches such as LIME and SHAP, alongside model-specific methods including decision trees and interpretable neural networks. We also address ante-hoc vs. post hoc strategies, local vs. global explanations, and emerging privacy-preserving techniques. To contextualize XAI’s growth, we integrate capital investment and publication trends, showing that research momentum has remained resilient despite market fluctuations. Finally, we propose a roadmap for 2025–2030, emphasizing evaluation standards, adaptive explanations, integration with Zero Trust architectures, and the development of self-explaining agents supported by global standards. By combining technical insights with societal implications, this article provides both a scholarly contribution and a practical reference for advancing trustworthy AI.
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ISSN:2673-2688
2673-2688
DOI:10.3390/ai6110285