Elliott Ash: Catalogue data in Autumn Semester 2022
|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|
|Relationship||Assistant Professor (Tenure Track)|
|851-0760-00L||Building a Robot Judge: Data Science for Decision-Making |
Particularly suitable for students of D-INFK, D-ITET, D-MTEC
|3 credits||2V||E. Ash|
|Abstract||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.|
|Objective||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.|
|Content||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.
|851-0761-00L||Building 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 credits||2V||E. Ash|
|Abstract||Students 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.|
|Objective||In 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.|
|Content||Students 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.
|876-0201-00L||Technology and Policy Analysis||8 credits||5G||T. Schmidt, E. Ash, F. M. Egli, R. Garrett, M. Leese, A. Rom, B. Steffen|
|Abstract||Technologies substantially affect the way we live and how our societies function. Technological change, i.e. the innovation and diffusion of new technologies, is a fundamental driver of economic growth but can also have detrimental side effects. This module introduces methods to assess technology-related policy alternatives and to analyse how policies affect technological changes and society.|
Participants understand (1) what ex ante and ex post policy impact analysis is, (2) in what forms and with what methods they can be undertaken, (3) why they are important for evidence-based policy-making.
Analysis of Policy and Technology Options:
Participants understand (1) how to perform policy analyses related to technology; (2) a policy problem and the rationale for policy intervention; (3) how to select appropriate impact categories and methods to address a policy problem through policy analysis; (4) how to assess policy alternatives, using various ex ante policy analysis methods; (5) and how to communicate the results of the analysis.
Evaluation of Policy Outcomes:
Participants understand (1) when and why policy outcomes can be evaluated based on observational or experimental methods, (2) basic methods for evaluating policy outcomes (e.g. causal inference methods and field experiments), (3) how to apply concepts and methods of policy outcome evaluation to specific cases of interest.
Big Data Approaches to Policy Analysis:
Participants understand (1) why "big data" techniques for making policy-relevant assessments and predictions are useful, and under what conditions, (2) key techniques in this area, such as procuring big datasets; pre-processing and dimension reduction of massive datasets for tractable computation; machine learning for predicting outcomes; interpreting machine learning model predictions to understand what is going on inside the black box; data visualization including interactive web apps.
|Literature||Course materials can be found on Moodle.|