Elliott Ash: Catalogue data in Autumn Semester 2020

Name Prof. Dr. Elliott Ash
FieldLaw, Economics, and Data Science
Address
Recht, Ökonomie und Datenwiss.
ETH Zürich, IFW E 47.1
Haldeneggsteig 4
8092 Zürich
SWITZERLAND
Telephone+41 44 633 89 62
E-mailelliott.ash@gess.ethz.ch
DepartmentHumanities, Social and Political Sciences
RelationshipAssistant Professor

NumberTitleECTSHoursLecturers
851-0760-00LBuilding a Robot Judge: Data Science for Decision-Making Restricted registration - show details
Particularly suitable for students of D-INFK, D-ITET, D-MTEC
3 credits2VE. Ash
AbstractThis course explores the automation of decisions in the legal system. We delve into the machine learning tools needed to predict judge decision-making and ask whether techniques in model explanation and algorithmic fairness are sufficient to address the potential risks.
ObjectiveThis course introduces students to the data science tools that may provide the first building blocks for a robot judge. While building a working robot judge might be far off in the future, some of the building blocks are already here, and we will put them to work.
ContentData science technologies have the potential to improve legal decisions by making them more efficient and consistent. On the other hand, there are serious risks that automated systems could replicate or amplify existing legal biases and rigidities. Given the stakes, these technologies force us to think carefully about notions of fairness and justice and how they should be applied.

The focus is on legal prediction problems. Given the evidence and briefs in this case, how will a judge probably decide? How likely is a criminal defendant to commit another crime? How much additional revenue will this new tax law collect? Students will investigate and implement the relevant machine learning tools for making these types of predictions, including regression, classification, and deep neural networks models.

We then use these predictions to better understand the operation of the legal system. Under what conditions do judges tend to make errors? Against which types of defendants do parole boards exhibit bias? Which jurisdictions have the most tax loopholes? Students will be introduced to emerging applied research in this vein. In a semester paper, students (individually or in groups) will conceive and implement an applied data-science research project.
851-0761-00LBuilding a Robot Judge: Data Science for Decision-Making (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 credits2VE. Ash
AbstractStudents investigate and implement the relevant machine learning tools for making legal predictions, including regression, classification, and deep neural networks models. This is the extra credit for a larger course project for the course.
ObjectiveIn a semester paper, students (individually or in groups) will conceive and implement their own research project applying natural language tools to legal texts. Some programming experience in Python is required, and some experience with NLP is highly recommended.
ContentStudents will investigate and implement the relevant machine learning tools for making legal predictions, including regression, classification, and deep neural networks models.
We will use these predictions to better understand the operation of the legal system. In a semester project, student groups will conceive and implement a research design for examining this type of empirical research question.