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from Part I - Methods of Comparative Law
Published online by Cambridge University Press: 26 January 2024
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, and 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 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.
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