Information about the law is stored in legal texts: legislation, administrative decrees, court decisions, and other legal writings. Lawyers use this information to apply and reason about the law, and to predict legal outcomes. Legal reasoning, analysing legal texts, and predicting legal outcomes can also be done, in part, by artificial intelligence (AI). More recently, researchers have developed legal information retrieval systems by effective use of sophisticated machine learning and natural language processing technologies on publicly available legal documents to assist legal practice. The availability of such legal information retrieval systems has created opportunities for improving the efficiency and consistency of existing legal systems. The main challenge for semantic analysis is that legal texts are predominantly unstructured data.
In this course, you will learn about the following major topics:
- Introduction to Artificial Intelligence and Law
- Legal Information Retrieval Systems
- Machine Learning with Legal Texts
- Natural Language Processing for Legal Texts
- Semantic Analysis of Legal Texts
- Large Language Models and Their Applications In Law
Upon completion of the course, you will be able to:
- Explain the methodology of legal information retrieval systems;
- Explain and apply fundamental concepts of legal artificial intelligence;
- Identify technical and legal challenges with legal artificial intelligence;
- Apply and evaluate machine learning methods for computational analysis of law;
- Leverage advanced prompting techniques to apply large language models in legal text analysis and question answering;
- Perform programming tasks to engage in legal text analysis, search and prediction.
| Week | Tasks | Notebook |
|---|---|---|
| 1 | Correlation, Covariance & Cross-Tabulations | ipynb |
| 2 | Regression | ipynb |
| 3 | Legal Judgment Prediction | ipynb |
| 4 | Large Language Models, Legal Question Answering and Legal Information Retrieval | ipynb |