Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges
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XAI has recently become a hot research topic, with new methods developed.
“Recently, eXplainable-AI (XAI) [179] has become a hot research topic, with new methods developed [180,181].”
From A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects (2023) · cites this paper as [181]
The reliability and predictive accuracy of an AI model is often inversely correlated with its interpretability; the better the predictive accuracy, the less explainable the model.
“The reliability and predictive accuracy of an AI model is often inversely correlated with its interpretability as shown in Fig. 9; the better the predictive accuracy, the less explainable the model [45].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [45]
ISO/TC 204 is the main standard facilitating the systematic use of autonomous vehicle technology and delivers an extensive manual on the entire system and infrastructure elements of ITS.
“The main standard facilitating the systematic use of autonomous vehicle technology in this context is ISO/TC 204, which delivers an extensive manual on the entire system and infrastructure elements of ITS [45], [79], [80].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [45]
Articulating all of the system's components and operations is a barrier when trying to break the system down into its constituent aspects because all input variables have to be easy to comprehend.
“Since all of the input variables have to be easy to comprehend, articulating all of the system's components and operations is a barrier when trying to break the system down into its constituent aspects [45], [51].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [45]
Explainable AI is proposed as a viable solution to address the challenges posed by the black-box nature of AI, ML, and DL models.
“In this context, and to address the challenges posed by the black-box nature of AI, ML, and DL models, explainable AI (XAI) is proposed as a viable solution.”
From Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review (2023) · cites this paper as [137]
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Explainable Artificial Intelligence: a Systematic Review
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- Authors
- Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J.
- Affiliations
- Lenovo (China) (CN)
- German Research Centre for Artificial Intelligence (DE)
- Peking University (CN)
- Tsinghua University (CN)
- Corresponding author
- Xu, Feiyu
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 563-574
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2975495759
- Cited by
- 758
- References
- 20
- Citations by year
- 2026: 70 · 2025: 185 · 2024: 193 · 2023: 158 · 2022: 76 · 2021: 62 · 2020: 12 · 2019: 2
- FWCI
- 14.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- explainable AI, explainability, deep neural network, black-box models, expert systems, future research directions
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