Cristina Caja Moya, Elio Quiroga Rodriguez

Más allá de la caja negra: la IA en los juzgados del orden civil (una propuesta heurística)

Introduction

Más allá de la caja negra: la ia en los juzgados del orden civil (una propuesta heurística). Implementación de IA en juzgados civiles. Analiza desafíos, fiabilidad y explicabilidad de modelos neuronales (BERT). Propuesta heurística para asistir a profesionales jurídicos.

Abstract

El artículo explora la potencial implementación de modelos de inteligencia artificial basados en redes neuronales supervisadas en el ámbito de la jurisdicción civil, analizando detalladamente las posibilidades y desafíos de introducir tecnologías avanzadas como los modelos transformadores en el sistema judicial. Se propone una metodología experimental que permitiría evaluar la fiabilidad, precisión y capacidad explicativa de estos modelos, utilizando como campo de pruebas los juzgados civiles españoles, aprovechando la estructura jerárquica y predecible de su legislación, la amplia disponibilidad de datos históricos etiquetados y la relativa baja complejidad ética de ciertos procedimientos civiles en comparación con otras jurisdicciones. El estudio profundiza en las características técnicas que hacen de los modelos como BERT, herramientas prometedoras para asistir a los profesionales jurídicos, destacando especialmente su capacidad para comprender contextos legales, identificar patrones en la jurisprudencia y ofrecer explicaciones transparentes de sus decisiones.

Review

The article, "Más allá de la caja negra: la IA en los juzgados del orden civil (una propuesta heurística)," addresses a profoundly relevant and timely subject: the integration of Artificial Intelligence, specifically supervised neural networks and transformer models, into civil jurisdiction. The abstract effectively outlines a critical exploration into the possibilities and inherent challenges of deploying such advanced technologies within the legal system. It introduces a compelling experimental methodology designed to assess the reliability, accuracy, and crucially, the *explainability* of these AI models, proposing the Spanish civil courts as an ideal testing ground due to their structured legislation, abundant historical data, and comparatively lower ethical complexity for initial deployment. This foundational approach promises to move beyond simplistic applications, aiming for a deeper, more transparent understanding of AI's role in judicial processes. A significant strength of this proposed research lies in its specific and well-justified focus. The selection of Spanish civil courts is particularly strategic, leveraging the predictable hierarchical nature of its legal framework and the noted availability of labeled historical data, which are critical prerequisites for training robust AI models. The emphasis on models like BERT, known for their contextual understanding and pattern recognition capabilities, directly addresses the sophisticated demands of legal interpretation. Furthermore, the abstract's commitment to evaluating not just precision but also "capacidad explicativa" (explanatory capacity) is commendable, directly tackling the "black box" criticism often leveled against AI. This commitment to transparency is vital for fostering trust and acceptance within the inherently conservative judicial environment, distinguishing this proposal from mere predictive model applications. While the abstract presents a robust and promising research agenda, a comprehensive study would benefit from a deeper consideration of a few points. Although "relative baja complejidad ética" (relatively low ethical complexity) is noted for certain civil procedures, a thorough discussion on how *any* remaining ethical dilemmas (e.g., potential for algorithmic bias in historical data, impact on human legal reasoning, effective human oversight) would be mitigated or addressed, even in these simpler cases, would further strengthen the proposal. Additionally, while data availability is mentioned, the practicalities of *curating, standardizing, and accessing* such data in a usable format for AI training often present significant logistical hurdles that warrant exploration. Nevertheless, this proposed work offers a highly valuable heuristic framework that promises significant contributions to the nascent field of AI in law. Its potential to demystify AI's application in courts and provide transparent, explainable insights makes it a vital and timely contribution to both legal tech and computational law discourse.

Full Text

Comments

You need to be logged in to post a comment.