AI Approaches to Predictive Justice: A Critical Assessment

This paper addresses the domain of predictive justice, exploring the intersection of artificial intelligence (AI) and judicial decision-making. We will first introduce the concept of predictive justice, referring to the ongoing debate surrounding the potential automation of judicial decisions through AI systems. Then, we will examine the current landscape of AI approaches employed in predictive justice applications, providing a comprehensive overview of methodologies and technological advancements. Then, we delve into the phenomenology of predictive justice, highlighting the diverse spectrum of legal predictions achievable with contemporary AI systems. We also assess the extent to which these predictive AI systems are presently integrated into real-world judicial practices. Finally, the paper critically addresses recurrent fears and critiques associated with predictive justice. We sort these critiques into unreasonable objections, reasonable concerns with possible technical solutions, and reasonable concerns demanding further investigation. Navigating the complexities of these critiques, we offer some insights for future research and practical implementation. The nuanced approach taken in this study contributes to the ongoing discourse on predictive justice, emphasising the need for a balanced evaluation of its potential benefits and legal challenge

Consent to Targeted Advertising

Targeted advertising in digital markets involves multiple actors collecting, exchanging, and processing personal data for the purpose of capturing users’ attention in online environments. This ecosystem has given rise to considerable adverse effects on individuals and society, resulting from mass surveillance, the manipulation of choices and opinions, and the spread of addictive or fake messages. Against this background, this article critically discusses the regulation of consent in online targeted advertising. To this end, we review EU laws and proposals and consider the extent to which a requirement of informed consent may provide effective consumer protection. On the basis of such an analysis, we make suggestions for possible avenues that may be pursued

L'azienda medium di se stessa. Nel mercato ipercompetitivo, spazio ai contenuti

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Complicaciones del abordaje anterior de columna lumbar en una serie de 197 pacientes

Objetivo: Analizar las complicaciones relacionadas con la cirugía de columna lumbar por vía anterior. Materiales y Métodos: Estudio descriptivo y retrospectivo de una serie de pacientes operados por abordaje anterior de la columna lumbar entre 2006 y 2019. La población estaba formada por 197 pacientes. Las variables consideradas fueron: edad, sexo, diagnóstico, plan quirúrgico (artrodesis anterior, doble vía combinada, revisión anterior, extracción del implante), niveles lumbares involucrados, complicaciones intraquirúrgicas inmediatas, tempranas o tardías. Se utilizó la clasificación de Clavien-Dindo para las complicaciones quirúrgicas. Resultados: Se evaluó a 197 pacientes, con una edad promedio de 53.39 años (106 mujeres, 53,81% y 91 hombres, 46,19%). El diagnóstico más frecuente fue discopatía degenerativa en 51 pacientes (25,89%). Treinta y cuatro (17,26%) sufrieron complicaciones: 4 inmediatas (2,03%), 22 (11,16%) tempranas y 9 (4,57%) tardías. La complicación inmediata más frecuente fue la lesión arterial (2 pacientes). La complicación temprana más frecuente fue la lesión del platillo vertebral (5 pacientes). La complicación tardía más frecuente fue la fractura del cuerpo vertebral (4 pacientes), dos pacientes fallecieron como consecuencia de las complicaciones. Conclusión: En nuestra serie, las complicaciones más frecuentes fueron: lesión vascular (inmediata), lesión del platillo vertebral (temprana) y fractura del cuerpo vertebral (tardía)

L'utilizzo dei big data e dell'IA per una migliore qualità della regolamentazione

This paper analyses the prospects of using big data and artificial intelligence (AI) technologies to improve legislation (better regulation), in particular by means of impact assessment. After a brief background on the topic of better regulation, the new paradigm based on big data and AI will be investigated. The three phases of the data-centred regulation cycle will be tackled in succession, namely, (1) data collection and creation, (2) ex-ante impact analysis for policy planning and regulation design, and (3) ex-post monitoring and evaluation. Finally, the challenges for an acceptable use of AI and big data will be explored, focusing on the need to conform public action to a legal-ethical framework for data management and use

Predicting outcomes of Italian VAT decisions

This study aims at predicting the outcomes of legal cases based on the textual content of judicial decisions. We present a new corpus of Italian documents, consisting of 226 annotated decisions on Value Added Tax by Regional Tax law commissions. We address the task of predicting whether a request is upheld or rejected in the final decision. We employ traditional classifiers and NLP methods to assess which parts of the decision are more informative for the task

Combining WordNet and Word Embeddings in Data Augmentation for Legal Texts

Creating balanced labeled textual corpora for complex tasks, like legal analysis, is a challenging and expensive process that often requires the collaboration of domain experts. To address this problem, we propose a data augmentation method based on the combination of GloVe word embeddings and the WordNet ontology. We present an example of application in the legal domain, specifically on decisions of the Court of Justice of the European Union. Our evaluation with human experts confirms that our method is more robust than the alternatives

Detecting Arguments in CJEU Decisions on Fiscal State Aid

The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers

Deep Learning for Detecting and Explaining Unfairness in Consumer Contracts

Consumer contracts often contain unfair clauses, in apparent violation of the rel- evant legislation. In this paper we present a new methodology for evaluating such clauses in online Terms of Services. We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set. Our experimental study is based on deep neural net- works that aim to combine learning and reasoning tasks, one major example being Memory Networks. Preliminary results show that this approach may not only pro- vide reasons and explanations to the user, but also enhance the automated detection of unfair clauses

A star tracker for LSPE-STRIP

Versione finale. Final Version.This document describes the specifications for the Star Tracker (henceforth, STR) to be used with the LSPE/Strip instrument, as well as its use case and its design