Elliott Ash: Katalogdaten im Herbstsemester 2024

Name	Herr Prof. Dr. Elliott Ash
Lehrgebiet	Recht, Ökonomie und Datenwissenschaften
Adresse	Recht, Ökonomie und Datenwiss. ETH Zürich, IFW E 47.1 Haldeneggsteig 4 8092 Zürich SWITZERLAND
Telefon	+41 44 633 89 62
E-Mail	elliott.ash@gess.ethz.ch
Departement	Geistes-, Sozial- und Staatswissenschaften
Beziehung	Ausserordentlicher Professor

Nummer	Titel	ECTS	Umfang	Dozierende
851-0760-00L	Building a Robot Judge: Data Science for Decision-Making Findet dieses Semester nicht statt. Particularly suitable for students of D-INFK, D-ITET, D-MTEC.	3 KP	2V	E. Ash
Kurzbeschreibung	This 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.
Lernziel	This 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.
Inhalt	Data 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.
851-0763-00L	Supervised Research (Law, Economics, and Data Science) Findet dieses Semester nicht statt.	3 KP		E. Ash
Kurzbeschreibung	This is a supervised student project for 3 ECTS, supervised by the professorship of Elliott Ash (D-GESS). Students will adapt tools from econometrics and machine learning to questions in law, data science, and social science. Students must have some data science and/or statistics experience. Some programming experience in Python, Stata, or R is required.
Lernziel	Apply tools from data science and social science to a new project, potentially in a group, to develop a paper or app.
Voraussetzungen / Besonderes	Some programming experience in Python, Stata, or R is required. Some experience with data science or statistics is required.