# Explainable machine learning multi-label classification of Spanish legal judgements

arXiv: https://arxiv.org/abs/2405.17610 · HTML: https://mlsanity.com/paper/2405.17610/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 55 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 5 of 5)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification](https://mlsanity.com/paper/1911.01658/) (2019)
- [EVE: Explainable Vector Based Embedding Technique Using Wikipedia](https://mlsanity.com/paper/1702.06891/) (2017)

## Built on by (most cited first, 5 of 5)

- [LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning](https://mlsanity.com/paper/2506.07443/) (2025)
- [MARRO: Multi-headed Attention for Rhetorical Role Labeling in Legal Documents](https://mlsanity.com/paper/2503.10659/) (2025)
- [Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models](https://mlsanity.com/paper/2411.02036/) (2024)
- [Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data](https://mlsanity.com/paper/2406.12762/) (2024)
- [Detecting and explaining postpartum depression in real-time with generative artificial intelligence](https://mlsanity.com/paper/2508.10025/) (2025)

## Abstract (arXiv)

Artificial Intelligence techniques such as Machine Learning (ML) have not been exploited to their maximum potential in the legal domain. This has been partially due to the insufficient explanations they provided about their decisions. Automatic expert systems with explanatory capabilities can be specially useful when legal practitioners search jurisprudence to gather contextual knowledge for their cases. Therefore, we propose a hybrid system that applies ML for multi-label classification of judgements (sentences) and visual and natural language descriptions for explanation purposes, boosted by Natural Language Processing techniques and deep legal reasoning to identify the entities, such as the parties, involved. We are not aware of any prior work on automatic multi-label classification of legal judgements also providing natural language explanations to the end-users with comparable overall quality. Our solution achieves over 85 % micro precision on a labelled data set annotated by legal experts. This endorses its interest to relieve human experts from monotonous labour-intensive legal classification tasks.