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

SemesterSpring Semester 2020
LecturersE. Ash
Periodicityyearly recurring course
Language of instructionEnglish
CommentParticularly suitable for students of D-INFK, D-ITET, D-MTEC



Courses

NumberTitleHoursLecturers
851-0739-01 VSequencing Legal DNA: NLP for Law and Political Economy2 hrs
Mon13:15-15:00LFW C 5 »
E. Ash

Catalogue data

AbstractThis 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.
ObjectiveLaw 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.
ContentNLP 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 / NoticeSome programming experience in Python is required, and some experience with NLP is highly recommended.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersE. Ash
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

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Only public learning materials are listed.

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Offered in

ProgrammeSectionType
Data Science MasterInterdisciplinary ElectivesWInformation
Doctoral Department of Humanities, Social and Political SciencesDoctoral and Post-Doctoral CoursesWInformation
GESS Science in PerspectiveLawWInformation
GESS Science in PerspectiveD-INFKWInformation
GESS Science in PerspectiveD-ITETWInformation
GESS Science in PerspectiveD-MTECWInformation