Abstract
Law t reacts to the progression of scientific technology in the end. Though conservative, changes are beginning to take place due to Artificial Intelligence (AI). AI is automating conventional legal works, creating a new industry namely Legal Tech. This paper investigates the characteristics and flow of legal AI and computational law while focusing on the applicability of AI to international law. Mainly, the paper reviews three critical areas: dispute resolution, trial prediction, and machine translation, respectively. International law has different characteristics than domestic law applied in each country. Unlike domestic law, international law has not been aggregated from a pandect, and it is a still daunting task to draw any meaningful insights for further analysis due mainly to limited data (i.e., trial cases and precedents). Nevertheless, AI is already penetrating the legal ecology system, and international law would eventually accept the influx of such changes exhibiting greater force.
This book chapter is reproduction of “The Applicability of Artificial Intelligence in International Law,” Journal of East Asia and International Law 12(1): 7-30 under the official permission of YIJUN Press Ltd.
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Notes
- 1.
McCulloch and Pitts (1943).
- 2.
Rosenblatt (1957).
- 3.
For example, Regression, Decision Tree, Naive Bayes, Support Vector Machine, etc.
- 4.
Hinton et al. (2006).
- 5.
Krizhevsky et al. (2012).
- 6.
ROSS Intelligence is a legal searching service that harnesses the power of AI to make legal research more insightful.
- 7.
Symbolic approach refers to the research technique based on formal logic and symbol system in the field of inference, search, problem-solving and others. In plain language, it is an approach method that aims to solve the problems of the world through symbols and rules, just like mathematics.
- 8.
Liao (2005).
- 9.
Wong and Monaco (1995).
- 10.
Lederberg (1964).
- 11.
Ibid.
- 12.
- 13.
Buchanan and Headrick (1970).
- 14.
Ibid.
- 15.
McCarty (1977).
- 16.
It enables users to execute the work such as calculating the corporate tax with regards to capital transactions through computer programs by saving the details of law related to corporate tax in US as algorithm in the computer program.
- 17.
Case-Based System began from a research by Roger Schank from Yale University, US in the beginning of 1980s. It is the representative expert system methodology along with Rule-Based.
- 18.
Rissland and Ashley (1987).
- 19.
Sanders (1991).
- 20.
Paquin et al. (1991).
- 21.
Popple (1993).
- 22.
O’callaghan et al. (2003).
- 23.
Thiessen et al. (2012).
- 24.
Love and Genesereth (2005).
- 25.
Legal informatics is considered as a super ordinate concept of the science of law but after Machine Learning-Based AI technology has been applied, there is no significance in categorizing these two. However, in case of biology, Bioinformatics and Computational Biology are used differently.
- 26.
Carneiro (2014).
- 27.
Allen (1957).
- 28.
Mehl (1958).
- 29.
Ibid.
- 30.
Ibid.
- 31.
In 1987, International Conference on Artificial Intelligence and Law (ICAIL) was introduced. Through this international conference, scholars exchanged various information and computational law was disseminated not only in US but also around the world.
- 32.
Addady (2016).
- 33.
Porter (2018).
- 34.
Hibnick (2014).
- 35.
Gunst (2018).
- 36.
Troy (1969).
- 37.
Haggerty (2018).
- 38.
Lawyers in the US claimed that LegalZoom which uses science technology to provide legal counseling to customers was “Unauthorized Practice of Law.”
- 39.
See The Global Unicorn Club. In: CB Insights. https://www.cbinsights.com/research-unicorn-companies.
- 40.
(May 17, 2019) Company Overview of Blackstone Discovery Inc., Bloomberg.
- 41.
Lex Machina was taken over by an American legal information company, ‘LexisNexis’ and is evaluated as the representative successful model of LegalTech.
- 42.
Murray (2021).
- 43.
UN General Assembly Resolution 65/17 (2011).
- 44.
Joo (2016)
- 45.
Ibid.
- 46.
In the early 1980s, Rand was a company that advised on risk assessment in damage case. The system was to investigate the effect of changes in legal doctrine on settlement strategies and practices.
- 47.
Waterman and Peterson (1980).
- 48.
Beyond Win–Win. https://www.smartsettle.com.
- 49.
Kim (2016).
- 50.
Resolution Center. In: eBay. https://resolutioncenter.ebay.com.
- 51.
Rule and Rogers (2011).
- 52.
Solution Explorer. In: Civil Resolution Tribunal. https://civilresolutionbc.ca.
- 53.
Ibid.
- 54.
Kim. op. cit. 49.
- 55.
Sim (2018).
- 56.
Hutson (2017).
- 57.
Art. 7 of UNCITRAL Model Law on International Commercial Arbitration 2006.
- 58.
Art. V of Convention on the Recognition and Enforcement of Foreign Arbitral Awards 1958 (New York Convention).
- 59.
Art. II(2) of New York Convention.
- 60.
- 61.
Just like “ROSS” an Artificial Intelligence lawyer specializing in the Bankruptcy Law, all the analysis required for the final arbitration decision can be processed more efficiently.
- 62.
Paisle and Sussma (2018).
- 63.
Ibid.
- 64.
It will make available responses to detailed surveys to be completed by arbitration users who will report on their experiences with specific arbitrators. Arbitrator Intelligence has also collected almost 1,400 arbitral awards from jurisdictions around the world, which it intends to make available in some form.
- 65.
It collects arbitration-related data from critical sources including most of the major international arbitration institutions.
- 66.
It provides information about individual arbitrators which includes individual arbitrator’s own responses as to their procedural preferences and practices as well as providing names of counsel who have appeared before the arbitrator and arbitrators with whom they have sat on an arbitration panel.
- 67.
Trial Prediction or Predictive Trial refers to the set of efforts to predict a result of trial in advance which the author of this paper coined the terminology.
- 68.
- 69.
Based on a few characteristics (input value, independent variable), this technique analyzes the pattern existing between the label (response value, dependent variable) value and identifies a combination of predictable rules. It is similar to Twenty Questions as it poses questions and narrows down the subject.
- 70.
Martin et al. (2004).
- 71.
Ibid.
- 72.
Katz et al. (2014).
- 73.
Katz et al. (2017).
- 74.
This sentence synthesizes the findings from the aforementioned research articles.
- 75.
Aletras et al., op. cit. 68, at 20.
- 76.
Ibid.
- 77.
Cortes and Vapnik (1995).
- 78.
WTO, Dispute Settlement. https://www.wto.org/english/tratop_e/dispu_e/dispu_e.htm. See also International Criminal Tribunal for the former Yugoslavia (ICTY), Infographic: ICTY Facts & Figures. http://www.icty.org/en/content/infographic-icty-facts-figures; ICTY Judgment List. http://www.icty.org/en/cases/judgement-list.
- 79.
Pan and Yang (2009).
- 80.
Kennedy (2000).
- 81.
Weaver (1955).
- 82.
EC Joint Commission commenced the development of auto language translation system called EUROTRA in 1976 to reduce the colossal work and labor cost arising from the different languages, executing translation in 81 directions among 9 languages. At first, it was for 7 EC member countries like UK, France, Germany, Italy, Denmark, Netherlands and Greece but later on, Portugal and Spain were added so the development of translation system for 9 languages began in 1982 and was completed in 1993.
- 83.
Bahdanau et al. (2014).
- 84.
Hutchins (1995).
- 85.
Ibid.
- 86.
The principle of such method is simple. First, various meanings of a word or phrase are saved. It creates the so-called translation dictionary. Next, if the user inserts a sentence, it is divided into words or phrases and proposes the translation result that is judged to be closest to the original meaning.
- 87.
Wu et al. (2016).
- 88.
- 89.
Recently, LSTM (Long Short-Term Memory) which complements the disadvantages of early RNN is widely used.
- 90.
Zaremba et al. (2014).
- 91.
In particular, if the output has a sequence like the input data, it is called Sequence-to-Sequence (seq2seq) models. See generally Sutskever et al. (2014); Cho (2014). Seq2seq models have enjoyed great success in a variety of tasks such as machine translation (NMT), speech recognition, and text summarization.
- 92.
Cho et al., ibid.
- 93.
Ibid. at 1409.1259.
- 94.
Bae and Bae (2009).
- 95.
Laurie (2002).
- 96.
See Chosunilbo Daily (2011).
- 97.
The Korean version of Korea-US FTA and Korea-EU FTA are equivalent to the English version. As an equivalent text, there was discordance between the English and Korean version. This is, in fact, not mistranslation but is discordance in legal effect. Therefore, the translation of treaty has to be approached extremely carefully as it is not just a simple translation.
- 98.
Barrachina et al. (2009).
- 99.
Sarcevic (1997).
- 100.
Bae, Bae. op. cit. 94, at 28.
- 101.
Williams and Chesterman (2002).
- 102.
In 2011, the Ministry of Foreign Affairs and Trade revealed that as a result of inspecting the Korean copy of the Korea-EU FTA, there were more than 200 mistranslations. 296 errors in total, such as 166 cases of mistranslation, 9 cases of orthography, 25 cases of inconsistency, 13 errors in the indication of Proper Noun, were discovered, amended, and the revised agreement was disclosed.
- 103.
Yoo (2017).
- 104.
Papineni et al. (2002).
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Rhim, YY., Park, K. (2023). The Artificial Intelligence in International Law. In: Lee, E.Y.J. (eds) Revolutionary Approach to International Law. International Law in Asia. Springer, Singapore. https://doi.org/10.1007/978-981-19-7967-5_11
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