Elliott Ash: Catalogue data in Spring Semester 2020
|Name||Prof. Dr. Elliott Ash|
|Field||Law, Economics, and Data Science|
Recht, Ökonomie und Datenwiss.
ETH Zürich, IFW E 47.1
|Telephone||+41 44 633 89 62|
|Department||Humanities, Social and Political Sciences|
|851-0739-01L||Sequencing Legal DNA: NLP for Law and Political Economy|
Particularly suitable for students of D-INFK, D-ITET, D-MTEC
|3 credits||2V||E. Ash|
|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.|
|851-0739-02L||Sequencing Legal DNA: NLP for Law and Political Economy (Course Project)|
This is the optional course project for "Building a Robot Judge: Data Science for the Law."
Please register only if attending the lecture course or with consent of the instructor.
Some programming experience in Python is required, and some experience with text mining is highly recommended.
|2 credits||2V||E. Ash|
|Abstract||This is the companion course for extra credit for a more substantial project, for the course "Sequencing Legal DNA: NLP for Law and Political Economy".|
|860-0033-00L||Big Data for Public Policy |
Only for MSc STP, MSc CIS, PhD students D-GESS and D-MTEC.
STP students have priority.
|3 credits||2G||E. Ash, M. Guillot|
|Abstract||This course provides an introduction to big data methods for public policy analysis. Students will put these techniques to work on a course project using real-world data, to be designed and implemented in consultation with the instructors.|
|Content||Many policy problems involve prediction. For example, a budget office might want to predict the number of applications for benefits payments next month, based on labor market conditions this month. This course provides a hands-on introduction to the "big data" techniques for making such predictions. These techniques include:|
-- procuring big datasets, especially through web scraping or API interfaces, including social media data;
-- pre-processing and dimension reduction of massive datasets for tractable computation;
-- machine learning for predicting outcomes, including how to select and tune the model, evaluate model performance using held-out test data, and report results;
-- interpreting machine learning model predictions to understand what is going on inside the black box;
-- data visualization including interactive web apps.
Students will put these techniques to work on a course project using real-world data, to be designed and implemented in consultation with the instructors.