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

Case law plays a critical role in shaping our understanding of human rights, including the right to adequate housing. However, analyzing large legal databases like HUDOC, which contains over 40,000 cases, is a challenging task that requires automated solutions. This study focuses on detecting cases related to housing-a topic encompassing issues such as eviction, access to adequate housing and etc.-from the HUDOC database. For this, we developed classifiers to identify cases related to both housing and eviction issues. We first constructed a dataset using an unsupervised process refined through manual corrections. Then, we trained the Adaptive Chordal Distance-based Subspace Learning Vector Quantization models. These models achieved classification accuracies of 93% for housing-related cases and 91.5% for eviction-specific cases, matching the performance of transformer-based models while requiring fewer computational resources. Furthermore, they provide interpretability by assigning word-level importance scores, helping legal scholars understand and verify the reasoning behind the model's predictions. The models identified 2,305 potentially housing-related cases. Manual reviews confirmed that 278 of 340 reviewed cases were indeed relevant. By detecting overlooked cases and enriching legal datasets, this study highlights the utility of NLP methods in facilitating the analysis of human rights case law. This approach supports a deeper exploration of housing rights and eviction-related decisions under the European Court of Human Rights (ECtHR), offering transparency, efficiency, and scalability for legal research.