AI for Judges

Table of contents

Course overview

This module examines how tools from AI can help judges make decisions. Judges make decisions at several junctures of the justice system, pre- and post-trial. Their decisions concern matters of fact as well as matters of law. We will focus on decisions about matters of fact and examine three topics:

  1. Machine learning tools for risk assessment applied to decisions about bail, preventive detention and sentencing;

  2. Multiagent systems for deciding about the relevance and admissibility of evidence; and

  3. Argumentation structures and Bayesian networks for representing complex bodies of evidence.

Transversal to these topics, the module will also examine the different ways in which the judiciary can rely on findings about matters of fact that are entirely or partially based on automated elements, as well as the current legislative policies and case-law on the topic.

Instructors

Giulia Lasagni - University of Bologna - email: giulia DOT lasagni6 AT unibo DOT it

Marcello Di Bello - Arizona State University - email: mdibello AT asu DOT com

Shedule and course materials

Risk Assessment: Friday Nov 11, 2022: 17:00 - 19:00

The main course materials for this part of the course are:

For additional information:

Multiagent Systems: Saturday Nov 12, 2022: 10:00 - 12:00

The main course materials for this part of the course are:

For additional information:

Bayesian Networks: Saturday Nov 12, 2022: 14:00 - 16:00

The main course materials for this part of the course are:

For additional information about Bayesian networks, you may have a look at:

There are several software tools available to draw Bayesian Networks. Below are links to the most popular:

A recent and very good book on Bayesian networks, with an emphasis on legal applications: