An explainable approach to detect case law on housing and eviction issues within the HUDOC database

TL;DR

This paper develops an interpretable NLP model to identify housing and eviction-related cases in the HUDOC database, enhancing case categorization and uncovering overlooked relevant cases.

Contribution

The study introduces an explainable NLP approach for detecting housing and eviction issues in case law, improving interpretability and uncovering new relevant cases.

Findings

Models achieve performance comparable to complex methods

The approach provides explanations for decisions

New relevant cases were identified using the model

Abstract

Case law is instrumental in shaping our understanding of human rights, including the right to adequate housing. The HUDOC database provides access to the textual content of case law from the European Court of Human Rights (ECtHR), along with some metadata. While this metadata includes valuable information, such as the application number and the articles addressed in a case, it often lacks detailed substantive insights, such as the specific issues a case covers. This underscores the need for detailed analysis to extract such information. However, given the size of the database - containing over 40,000 cases - an automated solution is essential. In this study, we focus on the right to adequate housing and aim to build models to detect cases related to housing and eviction issues. Our experiments show that the resulting models not only provide performance comparable to more sophisticated approaches but are also interpretable, offering explanations for their decisions by highlighting the most influential words. The application of these models led to the identification of new cases that were initially overlooked during data collection. This suggests that NLP approaches can be effectively applied to categorise case law based on the specific issues they address.