Anonymisation of judicial decisions with machine learning

author: Aljaž Košmerlj, Jožef Stefan Institute
author: Matej Kovačič, CT3 Centre for Knowledge Transfer in Information Technologies, Jožef Stefan Institute
published: July 24, 2017,   recorded: May 2017,   views: 1107


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Slovenian Constitution determines that court proceedings are public. This means that court hearings (except when there are some special reasons, for instance involved minors, government secrets, etc.) shall be public, and judgments shall be pronounced publicly. Therefore public has the right to know the decisions of judiciary branch of power. However, publishing court decisions is also believed to create a push towards unification of jurisprudence. These are main reasons why Slovenian ministry of justice wants to publish all court decisions on the Internet. However, Slovenian Constitution also protects personal data, so court decisions should be published on the Internet in anonymous form. Before public release, all personal data or other data from which persons involved in trial could be identified, should be removed from court decision In a presentation we will present a tool Tacita, which helps in this anonymisation process. Tool was developed at Jožef Stefan Institute and uses machine learning to predict which part of a court decision should be removed (anonymised) with a high probability. Tacita is not working completely automatic, but helps in otherwise time-consuming manual process of anonymisation. We will also show how the tool has been developed and which tools for analyzing natural language has been used.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: