This paper provides an overview of machine learning models, as compared to traditional economic models. It also lays out emerging issues in law and economics that the machine learning methodology raises. In doing so, Asian contexts are considered. Law and economics scholarship has applied econometric models for statistical inferences, but law as social engineering often requires forward-looking predictions rather than retrospective inferences. Machine learning can be used as an alternative or supplementary tool to improve the accuracy of legal prediction by controlling out-of-sample variance along with in-sample bias and by fitting diverse models to data with non-linear or otherwise complex distribution. In the legal arena, the past experience of using economic models in antitrust and other high-stakes litigation provides a clue as to how to introduce artificial intelligence into the legal decision-making process. Law and economics is also expected to provide useful insights as to how to balance the development of the artificial intelligence technology with fundamental social values such as human rights and autonomy.
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