# Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators

arXiv: https://arxiv.org/abs/2404.00437 · HTML: https://mlsanity.com/paper/2404.00437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 63 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Explaining NonLinear Classification Decisions with Deep Taylor Decomposition](https://mlsanity.com/paper/1512.02479/) (2015)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Human-in-the-loop Artificial Intelligence](https://mlsanity.com/paper/1710.08191/) (2017)
- [Human-in-the-Loop Interpretability Prior](https://mlsanity.com/paper/1805.11571/) (2018)
- [Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensembles](https://mlsanity.com/paper/2005.04289/) (2020)
- [Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code](https://mlsanity.com/paper/2112.03033/) (2021)
- [Detection of financial opportunities in micro-blogging data with a stacked classification system](https://mlsanity.com/paper/2404.07224/) (2024)
- [Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media](https://mlsanity.com/paper/2006.05908/) (2020)
- [Extending Class Activation Mapping Using Gaussian Receptive Field](https://mlsanity.com/paper/2001.05153/) (2020)
- [Implementing local-explainability in Gradient Boosting Trees: Feature Contribution](https://mlsanity.com/paper/2402.09197/) (2024)
- [An Automated Text Categorization Framework based on Hyperparameter Optimization](https://mlsanity.com/paper/1704.01975/) (2017)

## Built on by (most cited first, 1 of 1)

- [Explainable assessment of financial experts' credibility by classifying social media forecasts and checking the predictions with actual market data](https://mlsanity.com/paper/2406.11924/) (2024)

## Abstract (arXiv)

Automatic legal text classification systems have been proposed in the literature to address knowledge extraction from judgments and detect their aspects. However, most of these systems are black boxes even when their models are interpretable. This may raise concerns about their trustworthiness. Accordingly, this work contributes with a system combining Natural Language Processing (NLP) with Machine Learning (ML) to classify legal texts in an explainable manner. We analyze the features involved in the decision and the threshold bifurcation values of the decision paths of tree structures and present this information to the users in natural language. This is the first work on automatic analysis of legal texts combining NLP and ML along with Explainable Artificial Intelligence techniques to automatically make the models' decisions understandable to end users. Furthermore, legal experts have validated our solution, and this knowledge has also been incorporated into the explanation process as "expert-in-the-loop" dictionaries. Experimental results on an annotated data set in law categories by jurisdiction demonstrate that our system yields competitive classification performance, with accuracy values well above 90%, and that its automatic explanations are easily understandable even to non-expert users.