Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220600212-7.doi: 10.11896/jsjkx.220600212

• Artificial Intelligence • Previous Articles     Next Articles

Explainability of Artificial Intelligence:Development and Application

WANG Dongli1, YANG Shan1, OUYANG Wanli2, LI Baopu3, ZHOU Yan1   

  1. 1 School of Automation and Electronics Information,Xiangtan University,Xiangtan,Hunan 411105,China;
    2 School of Electrical and Information Engineering,The University of Sydney,Sydney 2006,Australia;
    3 Baidu Research(USA),Sunnyvale,CA 94086,USA
  • Online:2023-06-10 Published:2023-06-12
  • About author:WANG Dongli,Ph.D,associate professor.Her main research interests include pattern recognition and distributed learning,intelligent decision-making and information processing. ZHOU Yan,Ph.D,professor.His main research interests include machine vision and cluster robotics,signal processing and information fusion.
  • Supported by:
    National Key Research and Development Program of China(2020YFA0713503),National Natural Science Foundation of China(61773330),Aeronautical Science Foundation of China(20200020114004) and Science and Technology Innovation Program of Hunan Province,China(2020GK2036).

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Abstract: In recent years,the extensive application of artificial intelligence in many fields and disciplines has shown its excellent performance.The improvement of this performance usually needs to sacrifice the transparency of the model.However,the complexity and black box nature of artificial intelligence models have become the main bottleneck in its application in high-risk fields,which seriously hinders the further application of artificial intelligence in specific fields.Therefore,it is urgent to improve the interpretability of the model to prove its reliability.Therefore,this paper introduces the typical models and methods of AI interpretability research from three aspects:machine learning model interpretability,deep learning model interpretability,and hybrid model interpretability,further describes the application of interpretable AI in teaching analysis,judicial judgment,and medical diagnosis,and summarizes and analyzes the shortcomings of existing interpretable methods,puts forward the development trend of the future research direction of AI interpretability,and hope to further promote the development and application of interpretability research.

Key words: Artificial intelligence, Machine learning, Deep learning, Interpretability

CLC Number: 

  • TP391
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