851-0760-00L  Building a Robot Judge: Data Science for Decision-Making

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



Courses

NumberTitleHoursLecturers
851-0760-00 VBuilding a Robot Judge: Data Science for Decision-Making Special students and auditors need a special permission from the lecturers.
The lecturers will communicate the exact lesson times of ONLINE courses.
2 hrs
Mon12-14ON LI NE »
E. Ash

Catalogue data

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.

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

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

General : Special students and auditors need a special permission from the lecturers

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