Machine-Learning Comparative Law

Forthcoming in Cambridge Handbook of Comparative Law, edited by Mathias Siems and Po Jen Yap (2023).

48 Pages Posted: 27 Jul 2022 Last revised: 3 Aug 2022

See all articles by Han‐Wei Ho

Han‐Wei Ho

Center for Empirical Legal Studies

Patrick Chung-Chia Huang

University of Chicago, Law School

Yun-chien Chang

Academia Sinica - Institutum Iurisprudentiae (IIAS)

Date Written: July 20, 2022

Abstract

Comparative lawyers are interested in similarities between legal systems. Artificial intelligence offers a new approach to understanding legal families. This chapter introduces machine-learning methods useful in empirical comparative law, a nascent field. This chapter provides a step-by-step guide to evaluating and developing legal family theories using machine-learning algorithms. We briefly survey existing empirical comparative law data sets, then demonstrate how to visually explore these using a data set one of us compiled. We introduce popular and powerful algorithms of service to comparative law scholars, including dissimilarity coefficients, dimension reduction, clustering, and classification. The unsupervised machine-learning method enables researchers to develop a legal family scheme without the interference from existing schemes developed by human intelligence, thus providing as a powerful tool to test comparative law theories. The supervised machine-learning method enables researchers to start with a baseline scheme (developed by human or artificial intelligence) and then extend it to previously unstudied jurisdictions.

Keywords: Unsupervised machine learning, dimension reduction, similarity measures, classification, clustering

JEL Classification: K11

Suggested Citation

Ho, Han-wei and Huang, Chung-Chia and Chang, Yun-chien, Machine-Learning Comparative Law (July 20, 2022). Forthcoming in Cambridge Handbook of Comparative Law, edited by Mathias Siems and Po Jen Yap (2023). , Available at SSRN: https://ssrn.com/abstract=4167440 or http://dx.doi.org/10.2139/ssrn.4167440

Han-wei Ho

Center for Empirical Legal Studies ( email )

128 Academia Sinica Rd., Sec. 2
Nankang
Taipei City, 11529
Taiwan

Chung-Chia Huang

University of Chicago, Law School ( email )

Yun-chien Chang (Contact Author)

Academia Sinica - Institutum Iurisprudentiae (IIAS) ( email )

128 Academia Sinica Rd., Sec. 2
Nankang
Taipei City, 11529
Taiwan

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