Ho, Huang & Chang on Machine Learning Comparative Law

Han‐Wei Ho (IIAS), Patrick Chung-Chia Huang (U Chicago Law, students), and Yun-chien Chang (IIAS) have posted “Machine Learning Comparative Law” (Cambridge Handbook of Comparative Law, Siems and Yap eds. (2023)) on SSRN. Here is the 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.