A Hierarchical Neural Framework for Classification and its Explanation in Large Unstructured Legal Documents

Automatic legal judgment prediction and its explanation suffer from the problem of long case documents exceeding tens of thousands of words, in general, and having a non-uniform structure. Predicting judgments from such documents and extracting their explanation becomes a challenging task, more so on documents with no structural annotation. We define this problem as "scarce annotated legal documents" and explore their lack of structural information and their long lengths with a deep-learning-based classification framework which we call MESc; "Multi-stage Encoder-based Supervised with-clustering"; for judgment prediction. We explore the adaptability of LLMs with multi-billion parameters (GPT-Neo, and GPT-J) to legal texts and their intra-domain(legal) transfer learning capacity. Alongside this, we compare their performance and adaptability with MESc and the impact of combining embeddings from their last layers. For such hierarchical models, we also propose an explanation extraction algorithm named ORSE; Occlusion sensitivity-based Relevant Sentence Extractor; based on the input-occlusion sensitivity of the model, to explain the predictions with the most relevant sentences from the document. We explore these methods and test their effectiveness with extensive experiments and ablation studies on legal documents from India, the European Union, and the United States with the ILDC dataset and a subset of the LexGLUE dataset. MESc achieves a minimum total performance gain of approximately 2 points over previous state-of-the-art proposed methods, while ORSE applied on MESc achieves a total average gain of 50% over the baseline explainability scores

Desarrollo de un modelo basado en analítica de datos que permite explicar la reincidencia delictiva de una persona condenada que se encuentre o haya estado bajo la vigilancia del instituto nacional penitenciario y carcelario INPEC

Maestría en Gestión Estratégica de la información, Facultad de Ciencias e Ingeniería.Este estudio tiene como objetivo el entendimiento de la reincidencia delictiva partiendo de una serie de características o variables de las personas que fueron condenadas. Entender la reincidencia delictiva es muy relevante dado que ella agrava las cifras de sobrepoblación de los establecimientos de reclusión en Colombia, la cual se traduce en hacinamiento e impacto negativo del proceso de resocialización de las personas condenadas. Para desarrollar los objetivos del proyecto se utilizó la metodología de proyectos de minería de datos llamada crisp-dm, con ella se lograron entender y preparar los datos, modelar los diferentes algoritmos usados en la investigación y finalmente realizar análisis y evaluación de los resultados. Se pudo concluir que existen variables que impactan en mayor o menor medida la reincidencia delictiva, lo cual permitió clasificar correctamente tanto a personas reincidentes como no reincidentes con un acierto del 76%. Entender la reincidencia delictiva y cuales son aquellas variables que más inciden en ella podría aportar beneficios sociales para el estado mediante el acompañamiento gubernamental de los perfiles más vulnerables. También se podrían obtener beneficios económicos mediante la reducción de los índices de reincidencia logrando con ello, la disminución de la responsabilidad económica que representan las personas privadas de la libertad para el estado

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio