Thư mời tham dự: Webinar Series #4: Analytical Methods in Law and Economics

18/10/2021

Viện Nghiên cứu Kinh Doanh - UEH trân trọng kính mời Quý Thầy cô và Anh/chị đăng ký tham gia chuỗi Webinar do Asian Law and Economics Association – Đối tác của Viện tổ chức.

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About the webinar series

In this webinar series, law and economics scholars will present their current research projects and guide the audience through the analytical techniques used in their research. The series aspires to be instructive for entry-level researchers and graduate students and to be informative for more established scholars. The time duration is one hour, including a Q&A period.

Topic: "Deploying Machine Learning Techniques for Legal Analysis"

Summary

This lecture provides an overview of machine learning (ML) models as compared to traditional economic models. Law and economics scholarship has applied econometric models for statistical inferences, but the law as social engineering often requires forward-looking predictions rather than retrospective inferences. ML 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 addition, recent progress in natural language processing (NLP) techniques has made it possible to build a powerful analytic model on complicated legal texts without line-by-line coding. This lecture discusses key ML/NLP techniques (such as algorithms, train-test split, regularization, and cross-validation), their application to legal analysis (including intellectual property, antitrust, criminal justice, and civil dispute resolution), and ongoing challenges and limitations.
 
 
Presenter: Professor Sangchul Park, Seoul National University School of Law
 
Sangchul Park is an assistant professor at Seoul National University School of Law. He completed his JSD at the University of Chicago and his undergraduate studies at Seoul National University. His main research area is the application of machine learning to legal studies and the legal implications of AI-induced societal challenges. He is teaching, at the law school, artificial intelligence & law and information & telecommunications law, and at the engineering school, smart city policy. Prior to beginning his academic career, he spent more than 13 years in private practice specializing in technology, media, and telecommunications.
 
Time: 6pm, Oct 20th, 2021 (Wed)
 
 

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