The Applicability of Artificial Intelligence in International Law

Author:Young-Yik Rhim - KyungBae Park
Position:The first and corresponding author. Representative Lawyer and CEO of Intellicon Metalab; Adjunct Professor of Mass Communication and Public Relation at Konkuk University (Seoul, Korea); Member and Judge of ICAIL; Vice President of Korean Association for Artificial Intelligence and Law. ORCID: https://orcid.org/0000-0001-9252-4159. The author...
Pages:7-30
Young-Yik Rhim
& KyungBae Park
∗∗
   

    
    


     
     
      

    

Keywords
International Law, Artificial Intelligence, Machine Learning, Deep
Learning, Computational Law, Legal-Tech, ODR, Trial Prediction, Machine
translation
The first and corresponding author. Representative Lawyer and CEO of Intellicon Metalab; Adjunct Professor of Mass
Communication and Public Relation at Konkuk University (Seoul, Korea); Member and Judge of ICAIL; Vice President of
Korean Association for Artificial Intelligence and Law. ORCID: https://orcid.org/0000-0001-9252-4159. The author may
be contacted at: ceo@intellicon.co.kr / Address: 554, Nonhyeon-ro, Gangnam-gu, Seoul 06136 Korea.
∗∗ The second author. Researcher at Intellicon Metalab. Ph.D.(Kyungbook N.U.) The author may be contacted at: kbp@
intellicon.co.kr
The Applicability


J. EAST ASIA & INTL L. Vol. 12/No.1 (2019); 7-30
Publication type : Research Article
Section : Issue Focus
DOI : http://dx.doi.org/10.14330/jeail.2019.12.1.01
8 Rhim & Park

 
(). They have become familiar to us after AlphaGo. However, its history dates
       
Neural Network (ANN) which emulates the human- neural networks. McCulloch
and Pitts (1943)
1
proposed the first mathematical model of the ANN. Using the
McCulloch-Pitts model, Frank Rosenblatt (1957)
2
invented the ANN-Algorithm
(Perceptron). ANN and Perceptron created the concept of Machine Learning which
      
     
    
classified into Rule-based and Learning-based (Machine learning). These two
methodologies have advantages and disadvantages, respectively.
   
3
other than ANN, but ANN is
the representative algorithm. ANN created a concept called Machine Learning and
     
      
       
      
published a monumental paper
4
with the support of Canada. This paper revealed
that the chronic issues of the ANN could be resolved through pre-training of data
and marked a new milestone in the research on ANN. After this paper, the word,
Deep learning   
5
the Hinton team won with
overwhelming performance at the   
Competition (),   
      
     
   
1 W. McCulloch & W. Pitts, A Logical Calculus of the Ideas Immanent in Nervous Activity, 5 BULL. MATH. BIOPHYSICS
115-33 (1943).
2 F. ROSENBLATT, THE PERCEPTRON, A PERCEIVING AND RECOGNIZING AUTOMATON PROJECT PARA 1-29 (Cornell Aeronautical
Laboratory ed., 1957).
3 For example, Regression, Decision Tree, Naive Bayes, Support Vector Machine, etc.
4 G. Hinton, et al., A Fast Learning Algorithm for Deep Belief Nets, 18 NEURAL COMPUTATION 1527-54 (2006).
5 A. Krizhevsky, et al., Imagenet Classification with Deep Convolutional Neural Networks, 25(2) ADVANCES IN NEURAL
INFORMATION PROCESSING SYSTEMS 1097-105 (2012).

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