Advanced Legal Analytics
Full course description
Artificial intelligence (AI) has experienced an enormous development boost in recent years. Novel methods such as generative AI have led to a rapid acceleration in the capabilities of AI systems, particularly in the analysis of unstructured data such as text.
These advances make the application of AI highly relevant in the legal field, which is highly focused on textual documents such as laws, administrative decrees, court decisions, contracts and other legal texts. Actors in the legal field (e.g., lawyers, judges, litigators) analyze and use this information to perform legal tasks, such as providing legal advice, drafting legal arguments, or deciding cases.
The advanced legal analytics course offers students an interactive exploration of how AI can be used to analyze legal texts and perform tasks in the legal domain. Through hands-on Python programming tasks, students will gain experience in methods such as machine learning, large language models, data science, and Python programming. Not only will students gain an intuitive, hands-on understanding of the capabilities and shortcomings of such systems. They will also be equipped to use these technologies in their own work and legal practice.
Teaching methods:
- Lectures to introduce important topics relating to the course material.
- Weekly hands-on programming tutorials, where students use jupyter notebook to explore the concepts in a hands-on manner.
- Students will work on a legal analytics group project throughout the course.
Assessment methods:
Attendance and Student Participation during tutorials, Project Presentation and Project Report (including Code and Datasets).
Course objectives
Intended Learning Outcomes:
Upon completion of the course, a student is able to:
- Plan and conceptualize the use of AI methods to perform tasks in the legal domain, taking into account the current capabilities and shortcomings of such methods;
- Apply data science methods to ingest, visualize and clean real-world legal datasets to allow for effective machine learning;
- Leverage effective and advanced prompting strategies to effectively harness large language models (such as ChatGPT) for performing tasks in the legal domain, including drafting, question answering and summarization;
- Utilize traditional machine learning models to perform tasks in the legal domain;
- Evaluate (quantitatively and qualitatively) the results of an AI system to understand its real-world performance and risks;
- Interpret and communicate the findings, implications and limitations of the AI system when applied to legal datasets/tasks
- collaborate effectively in groups for a legal analytics research project while designing, implementing and evaluating the methodology of the AI system;
- defend the methodology and the results of the legal analytics project in a presentation, addressing potential critiques constructively.
Prerequisites
None
Recommended prior knowledge
Basic knowledge of Python programming is a strong requirement. It is highly recommended that you have completed the Legal Analytics course.
Recommended reading
Readings and programming resources will be provided on a weekly basis.