851-0739-01L Sequencing Legal DNA: NLP for Law and Political Economy

Semester	Spring Semester 2020
Lecturers	E. Ash
Periodicity	yearly recurring course
Language of instruction	English
Comment	Particularly suitable for students of D-INFK, D-ITET, D-MTEC

Abstract	This course explores the application of natural language processing techniques to texts in law, politics, and the news media. Students will put these tools to work in a course project.
Objective	Law is embedded in language. An essential task for a judge, therefore, is reading legal texts to interpret case facts and apply legal rules. Can an artificial intelligence learn to do these tasks? The recent and ongoing breakthroughs in natural language processing (NLP) hint at this possibility. Meanwhile, a vast and growing corpus of legal documents are being digitized and put online for use by the public. No single human could hope to read all of them, yet many of these documents remain untouched by NLP techniques. This course invites students to participate in these new explorations applying NLP to the law -- that is, sequencing legal DNA.
Content	NLP technologies have the potential to assist judges in their decisions by making them more efficient and consistent. On the other hand, legal language choices -- as in legal choices more generally -- could be biased toward some groups, and automated systems could entrench those biases. We will explore, critique, and integrate the emerging set of tools for debiasing language models and think carefully about how notions of fairness should be applied in this domain. More generally, we will explore the use of NLP for social science research, not just in the law but also in politics, the economy, and culture. In a semester paper, students (individually or in groups) will conceive and implement their own research project applying natural language tools to legal or political texts.
Prerequisites / Notice	Some programming experience in Python is required, and some experience with NLP is highly recommended.