Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges
| Autor: | Feiyu Xu, Jun Zhu, Wei Fan, Yangzhou Du, Hans Uszkoreit, Dongyan Zhao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Progress in artificial intelligence
business.industry Computer science Deep learning Decision tree Inference Context (language use) 02 engineering and technology computer.software_genre Data science Expert system 020204 information systems Transparency (graphic) Business decision mapping 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing |
| Zdroj: | Natural Language Processing and Chinese Computing ISBN: 9783030322359 NLPCC (2) |
| DOI: | 10.1007/978-3-030-32236-6_51 |
| Popis: | Deep learning has made significant contribution to the recent progress in artificial intelligence. In comparison to traditional machine learning methods such as decision trees and support vector machines, deep learning methods have achieved substantial improvement in various prediction tasks. However, deep neural networks (DNNs) are comparably weak in explaining their inference processes and final results, and they are typically treated as a black-box by both developers and users. Some people even consider DNNs (deep neural networks) in the current stage rather as alchemy, than as real science. In many real-world applications such as business decision, process optimization, medical diagnosis and investment recommendation, explainability and transparency of our AI systems become particularly essential for their users, for the people who are affected by AI decisions, and furthermore, for the researchers and developers who create the AI solutions. In recent years, the explainability and explainable AI have received increasing attention by both research community and industry. This paper first introduces the history of Explainable AI, starting from expert systems and traditional machine learning approaches to the latest progress in the context of modern deep learning, and then describes the major research areas and the state-of-art approaches in recent years. The paper ends with a discussion on the challenges and future directions. |
| Databáze: | OpenAIRE |
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