Law and Technology Workshops: AI for the (Empirical) Legal Scholar
Part 1: March 17, 2025 (Mon), 12pm – 2pm
Part 2: March 19, 2025 (Wed), 3pm – 5pm
Venue: Academic Conference Room, 11/F Cheng Yu Tung Tower, The University of Hong Kong
Speaker: Aniket Kesari (Associate Professor of Law, Fordham University School of Law)
What counts as “data” for empirical legal studies? Conventional statistical analysis of legal datasets is often confined to what can be easily quantified – the number of crimes committed following the passage of a law, how many legislators voted on an issue, etc. But law also shows up in other forms – in the opinions that judges write, the contracts that people form, and the arguments in a courtroom to name a few.
In these workshops, we will explore how machine learning can be used to analyze these unconventional forms of data. We will begin with building supervised and unsupervised machine learning models and apply them to policy problems. We will then use machine learning methods like sentiment analysis, word2vec, and transformers to analyze legal text. We will conclude with methods for analyzing images, audio, and video and see how machine learning can help us identify the pitch and tone of a speaker, analyze the complexity of trademarked images, and analyze judges’ behavior in a video. By the end of the workshop, participants will have tools to begin designing their own projects at the intersection of machine learning and law.
Aniket Kesari is an Associate Professor at Fordham Law School. His research focuses on law & technology, data science, and public policy. He uses techniques drawn from causal inference, machine learning, and natural language processing to investigate questions in law and tech, and he is also interested in integrating data science into empirical legal studies more broadly. Some of his recent scholarship looks at data breach notification laws, mandatory cybersecurity risk disclosures, privacy and algorithmic fairness, trademark search engines, and online hate speech. His work has appeared in law reviews (George Washington Law Review, Berkeley Technology Law Journal, Illinois Journal of Law, Technology, and Policy, NYU Journal of Legislation and Public Policy), peer-reviewed social science outlets (Journal of Empirical Legal Studies, Journal of Online Trust and Safety), and peer-reviewed computer science proceedings (Neural Information Processing Systems AI for Social Good Workshop, ACM Symposium on Computer Science and Law).
Moderator: Benjamin Chen, Associate Professor & Director of Law and Technology Centre, The University of Hong Kong Faculty of Law
To register for the workshops, please go to https://hkuems1.hku.hk/hkuems/ec_regform.aspx?guest=Y&UEID=98636.
For inquiries, please contact Ms. Grace Chan at / 3917 4727.