An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

In a wide range of industries and academic fields, artificial intelligence is becoming increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow in popularity and performance. Although AI models, particularly machine learning models, are successful in research,...

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Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 5; p. 1092
Main Authors Nagahisarchoghaei, Mohammad, Nur, Nasheen, Cummins, Logan, Nur, Nashtarin, Karimi, Mirhossein Mousavi, Nandanwar, Shreya, Bhattacharyya, Siddhartha, Rahimi, Shahram
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2023
Subjects
Accuracy
Algorithms
Artificial intelligence
Bibliometrics
Citations
Computational linguistics
Data mining
Decision making
Empirical analysis
Explainable artificial intelligence
Knowledge
Language processing
Machine learning
Natural language interfaces
Natural language processing
Scientometrics
United States
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Summary:In a wide range of industries and academic fields, artificial intelligence is becoming increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow in popularity and performance. Although AI models, particularly machine learning models, are successful in research, they have numerous limitations and drawbacks in practice. Furthermore, due to the lack of transparency behind their behavior, users need more understanding of how these models make specific decisions, especially in complex state-of-the-art machine learning algorithms. Complex machine learning systems utilize less transparent algorithms, thereby exacerbating the problem. This survey analyzes the significance and evolution of explainable AI (XAI) research across various domains and applications. Throughout this study, a rich repository of explainability classifications and summaries has been developed, along with their applications and practical use cases. We believe this study will make it easier for researchers to understand all explainability methods and access their applications simultaneously.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12051092