Artificial Intelligence and Legal Reasoning

Cet article vise à présenter les risques politiques, économiques et sociaux liés à l'utilisation de l'intelligence artificielle et comment les éviter. L'intelligence artificielle (IA) est une technologie transformatrice susceptible de révolutionner divers secteurs, mais elle présente également des risques politiques, économiques et sociaux importants. Ces risques ne peuvent être totalement éliminés, mais ils peuvent être réduits et maîtrisés grâce à des actions coordonnées entre les gouvernements, les entreprises, les instituts de recherche et la société. L'IA offre des opportunités transformatrices, mais elle présente également des risques importants qui nécessitent une attention immédiate et coordonnée. Il est crucial que les gouvernements, l'industrie, le monde universitaire et la société civile collaborent pour élaborer et mettre en œuvre des politiques et des stratégies qui maximisent les avantages de l'IA tout en évitant ses risques. L'avenir de l'IA n'est pas prédéterminé. Il sera façonné par les choix politiques et éthiques que nous faisons aujourd'hui. Éviter les risques nécessite un équilibre entre innovation et réglementation. L'IA doit être développée de manière éthique (respectant les valeurs humaines et les droits fondamentaux), inclusive (réduisant les inégalités au lieu de les accroître), transparente (explicable et vérifiable) et durable (visant à promouvoir le bien-être collectif). En bref : l’IA est un outil puissant, mais sans régulation, éthique et participation sociale, elle peut accroître la concentration du pouvoir, générer de nouvelles formes d’exclusion et saper les valeurs démocratiques.

In this paper, the eight truth values of U8 have been embedded in probability values to form a probability logic. The rules of probability are then given form in terms of the primary truth values {T, F, N}. In particular, negative and positive logic can be employed to calculate the primary truth values {T, F, N} for implication and the additive, multiplicative and complementary rule of probability. Bayesian probability is employed to describe implication and conditional probability and the Boolean rule for material implication, A OR B, is found to be irrelevant to U8.

This research aims to define an integrated methodology for modelling judgments, starting from legal texts and capturing not only structural parts but also arguments used by judges to reach conclusions. The goal is to build a complete ontology framework capable of detecting and modelling knowledge from the judgement's text. The formalized judgements provide the necessary metadata for the rule layer to enable argumentation towards the acceptance or rejection of a given interpretation. For pursuing this goal it is important to integrate the legal ontology construction with a rule formalization following legal reasoning-oriented theory and defeasible logics, just like the Carneades application (presented here) follows Walton's argumentation theory. The XML interchange uses OWL, RuleML, and the emerging LegalRuleML.

Depuis quelques années, nous assistons à un florilège de dispositifs vis-à-vis de l’IA : chartes, déclarations, ensemble de principes éthiques, etc. Ces structures s’inspirent directement de philosophies morales dominantes pareillement au déontologisme, ou encore à l’éthique conséquentialiste. Ainsi, la dimension éthique de certains usages de l’IA est déterminée par
ces philosophies morales dont les prémisses et les représentations qu’ils contiennent ne sont que très rarement remises en question. Il s’agira donc dans cet article de bouleverser ce que nous appelons « la saturation éthique de l’IA » en faisant appel à une théorie réellement alternative à savoir l’éthique du
care en tant qu’elle nous invite à changer de regard et à adopter de nouvelles perspectives critiques sur l’IA. Les éthiques du care marquent une rupture
nette en posant comme critère de moralité de l’action le soin apporté à autrui,
la considération de l’interdépendance du vivant et la prise en charge de la vulnérabilité comme caractère inhérent à l’espèce humaine (Brugère, 2011).
Les éthiques du care sont jugées féministes puisqu’elles mettent au cœur
du politique et de l’action collective le care comme socle du vivant, effectué traditionnellement et majoritairement par les femmes et les personnes historiquement marginalisées dans notre société, de manière gratuite ou peu reconnue (Gilligan, 2011). Nous nous poserons les questions suivantes : comment expliquer, dès lors, que les éthiques du care constituent un véritable angle mort des réflexions en éthique de l’IA ? L’IA est-elle éthique, si nous adoptons une perspective d’éthique du care ? Cette proposition est importante, car elle
permet d’une part d’élargir nos perspectives éthiques et critiques vis-à-vis
de l’IA et d’autre part, d’améliorer les politiques publiques en la matière
afin de les rendre plus justes et plus inclusives.

As it follows from Recital 20 as well as from the delimitation set out in its Article 2 in its material scope of application, the Regulation (EU) 2016/679-General Data Protection Regulation (GDPR)-is competent to apply to the court's activity and other judicial authorities, stating that "Union or Member State law could specify the processing operations and processing procedures in relation to the processing of personal data by courts and other judicial authorities." The GDPR exempts the processing of personal data "by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, including the safeguarding against and the prevention of threats to public security" [Article 2(2)(d), to which Directive (EU) 2016/680 of 27 April 2016 shall apply]. Recital 80 of the aforementioned Directive states in similar terms to the GDPR that: "While this Directive applies also to the activities of national courts and other judicial authorities, the competence of the supervisory authorities should not cover the processing of personal data where courts are acting in their judicial capacity, in order to safeguard the independence of judges in the performance of their judicial tasks." In view of this legal framework, the question of who should hold the status of (data) "controller" in the courts is still not a peaceful question. In this paper we shall develop our argument, pointing out that it should be the judges and courts, who would hold the role of "controller" for the processing of personal data when "acting in judicial capacity" as defined in the recent judgment of the Court of Justice of the European Union (CJEU) of 24 March 2022 in case C-245/20. Finally, we shall make a critical analysis of this question in the current regulation in Portugal and Spain about data protection and the "controller" in the courts ("Responsável pelo Tratamento" in Portuguese; "Responsable del Tratamiento" in Spanish). KEYWORDS: Personal data protection in the courtscontrollerjurisdictional and nonjurisdictional data filesjudicial independence-Portugal and Spain.

This paper presents six carefully constructed mathematical problems, each intended to challenge the symbolic, spatial, or logical reasoning capabilities of advanced artificial intelligence (AI) models. These problems span various mathematical disciplines, including number theory, algebra, geometry, recursion, functional analysis, and combinatorics. Each problem is presented with a detailed solution and step-by-step analysis to facilitate future benchmarking of AI systems and the training of machine reasoning engines.

L'éminente préoccupation de sécurité en matière d'intelligence artificielle est à la fois incontournable et complexe. Ainsi que l’a justement relevé B. Warusfel, « la technologie génère sa propre insécurité ». À travers ses phases successives, conception, déploiement et utilisation, l’intelligence artificielle met au défi le principe même de sécurité juridique. Or, cette dernière constitue, selon la célèbre formule de Carbonnier, un besoin juridique élémentaire, presque instinctif, voire « animal », qui sous-tend l’édifice normatif tout entier. Une problématique pertinente s’impose à cet égard, à savoir : en quoi le déploiement et l'utilisation croissante de l'intelligence artificielle remettent-ils en question les fondements de la sécurité juridique, et quelles mesures peuvent être envisagées pour harmoniser le progrès de l'IA avec la préservation d'un cadre juridique fiable et protecteur ?

La présente contribution se propose d’examiner la relation entre deux dimensions inextricables de la sécurité à l’ère numérique : la sécurité juridique et la sécurité technique, de disséquer leur intersection, car l’IA et la sécurité évolueront désormais en tandem, et enfin de formuler des recommandations pour concilier le progrès technologique et son encadrement juridique. L’approche méthodologique adoptée est interdisciplinaire, puisant dans des concepts et des connaissances issus du droit, des sciences sociales et de l’informatique. L’analyse conclut que la sécurité technique ne constitue pas seulement une condition du bon fonctionnement des systèmes d’IA, mais aussi un préalable indispensable à l’établissement d’un environnement juridique sûr et prévisible, rassurant tant les utilisateurs que les législateurs.

Artificial intelligence, through its ubiquitous algorithms, recommends, authorizes, forbids, empowers, and dictates, much like law does. As such, it introduces a new type of sui generis governance, which some refer to as "algorithmic governance" or "governance by numbers." This form of tacit and automated regulation differs from traditional technical regulation and surpasses the model of legal informatics. This governance relies on three overlapping and mutually reinforcing stages. First, there is the massive collection of data (data surveillance). Then, this data is aggregated, processed, and analyzed (data mining). Finally, this processed data influences behavior and guides decisions for various purposes. These processes allow algorithms to shape individual behaviors and direct user practices.

Thus, these predictive and statistical inference algorithms become producers of norms, acting as a "source" of postmodern law. Through this, artificial intelligence introduces a normativity distinct from state legislation, resembling a form of collective action mediated by algorithms rather than a direct governance based on individual autonomy. Normativity is then thought of in terms of programming rather than legislation. Recent literature agrees on recognizing this gradual shift from political, deliberate, and vertical governance to automatic, mathematical, and horizontal governance. Some observers even claim that we have moved from a law-based democracy to an algorithm-based dictatorship, marking a rupture in how norms are constituted and imposed. Unlike state normativity, which imposes laws and regulations from the outside, algorithmic governance relies on a form of immanent and invisible normativity. This manifests through the daily interactions between the user and the self-learning algorithm.

In this paper, we offer a reflection on the role of this emerging normativity in the regulation of artificial intelligence. We will examine the essential characteristics it should take on to exercise an effective normative function, complementary to law, while addressing the critiques and limitations inherent in this form of self-regulation.

Patrice Cardot envisage l'enseignement du futur dans un contexte de transformation technologique rapide, influencé par des forces néolibérales et des contraintes financières. Il souligne que l'éducation est stratégique et que ses contenus et méthodes servent des finalités politiques évidentes. La technologisation de l'éducation, notamment par l'intelligence artificielle (IA), est vue comme un instrument performant pour atteindre ces objectifs.

Cependant, il met en garde contre les risques et défis associés à ces évolutions rapides, qui dépassent souvent le rythme des débats politiques et des cadres réglementaires. L'Union européenne et l'UNESCO jouent un rôle crucial en promouvant un écosystème éducatif numérique performant et en veillant à ce que l'application de l'IA dans l'éducation respecte les principes d'inclusion et d'équité.

P. Cardot anticipe une éducation de plus en plus assistée par la technologie, évoluant potentiellement vers une éducation algorithmique, avec des MOOCs et une IA générative plus puissante. Il appelle à une approche humaniste de l'IA pour éviter de creuser les fossés technologiques et garantir que chacun bénéficie de la révolution technologique en cours.

Plus précisément, il aborde plusieurs aspects critiques concernant l'intégration de l'intelligence artificielle (IA) dans le système éducatif et ses implications institutionnelles.

Voici quelques points clés qui pourraient nécessiter des aménagements institutionnels :
* Critique de la Numérisation de l'Éducation : Le document souligne une critique forte de la numérisation excessive dans l'éducation, soulignant que cela peut être contre-productif et ne pas répondre aux besoins pédagogiques réels. Il suggère que les institutions éducatives devraient réévaluer l'utilisation des technologies numériques et s'assurer qu'elles sont utilisées de manière judicieuse et fondée sur des preuves.
* Évaluation des Technologies Éducatives : Il est mentionné que les technologies éducatives, comme les logiciels éducatifs, n'ont pas toujours produit les résultats escomptés. Cela appelle à une évaluation plus rigoureuse et critique des outils technologiques utilisés dans l'éducation pour s'assurer qu'ils apportent une réelle valeur ajoutée.
* Formation et Compétences des Enseignants : Le document met en lumière la nécessité de former les enseignants non seulement à l'utilisation des technologies numériques, mais aussi à leur évaluation critique. Cela pourrait inclure des programmes de formation continue pour les enseignants afin qu'ils puissent intégrer efficacement les technologies dans leur enseignement.
* Protection des Données et Vie Privée : Avec l'augmentation de l'utilisation des technologies numériques, la protection des données personnelles des élèves et des enseignants devient cruciale. Les institutions doivent mettre en place des politiques strictes pour protéger ces données et assurer la confidentialité.
* Inclusion et Accessibilité : Il est essentiel de garantir que l'utilisation des technologies numériques ne crée pas de nouvelles formes d'exclusion. Les institutions doivent veiller à ce que tous les élèves aient un accès équitable aux ressources éducatives, indépendamment de leur situation socio-économique.
* Régulation et Politique de l'IA : Le document souligne la nécessité d'une régulation appropriée de l'IA dans l'éducation pour éviter les abus et les dérives potentielles. Cela pourrait inclure la mise en place de cadres éthiques et juridiques pour l'utilisation de l'IA dans les écoles.
* Éducation aux Médias et à l'Information : Il est crucial d'éduquer les élèves (et les enseignants) sur l'utilisation responsable et critique des médias numériques et de l'information, pour qu'ils puissent naviguer de manière informée et sécurisée dans l'espace numérique.
* Collaboration et Participation : Le document appelle à une collaboration accrue entre les différentes parties prenantes (enseignants, élèves, parents, décideurs politiques) pour façonner des politiques éducatives qui soient à la fois innovantes et respectueuses des valeurs éducatives fondamentales.
Ces points montrent clairement que l'intégration de l'IA et des technologies numériques dans l'éducation nécessite une approche réfléchie et bien régulée pour maximiser les bénéfices tout en minimisant les risques et les inconvénients potentiels.
Si Patrice Cardot plaide pour une intégration réfléchie et régulée de la technologie dans l'éducation, afin de maximiser ses bénéfices tout en minimisant les risques pour les élèves et la société, il en demeure pas moins sceptique quant à l'existence en France, en Europe comme dans le reste du monde se présentant toujours comme libre d'une volonté politique de véritable régulation en ce sens.

Analyse des inquiétudes autour de l'IA Une question complexe qui divise experts et citoyens. L'intelligence artificielle (IA) offre des avancées majeures, mais soulève aussi des préoccupations éthiques, économiques, environnementales et sociétales.

This book uses Artificial Intelligence to expand the Mars 360 (mark of the beast) concept into a complete legal, military and governance framework that could establish a new order in any country overnight. This is an example of how AI is able to equip Mars 360 with an elaborate governance structure, laws, bill of rights, amendments, and a military security framework with just the push of a button. This combination of Mars 360 and AI has been given the title of "Gahala"

This is the mark of the beast system. The basic premise of Mars 360 is that Mars exerts a negative influence on humanity, both at the societal level and at the individual level. At the societal level, especially when Mars is within 30 degrees of the lunar node and behind the sun, major terror attacks and stock crashes occur. I have put out data that corroborates my thesis in the book "Temperature Perturbations." But I have also posited that it affects people at the individual level, whereby the position of Mars at the time a person is born, predicts where that person would display a consistent lack of regard for certain tasks, starting from childhood and going into adult hood. This is explained in another book entitled "The Mars 360 Religious and Social System."

I have studied thousands of birthcharts, and have devised a system that separated all of humanity into six categories based on this Mars influence, with each category denoting exactly how this lack of regard would manifest in the person's life. The gist of why this is called the mark of the beast is due to the fact of the underlying assumption of Mars 360 is that these negative qualities are par for the course of how biological processes are affected by nature, with humans, much like other organisms, being subject to those biological processes which are triggered at the astrophysical level. For this reason, the idea of sin or iniquity being applied to this natural inclination loses traction under Mars 360. This is why Mars 360 is an aberration from the Abrahamic perspective.

There are six sectors. Each sector corresponds with a certain brain function which permits humans to carry out a number of tasks. For instance, the first sector ruling the occipital lobe affects our perception and face to face communication and other people's money. Mars appearing here at the time a person is born promotes a lack of regard for these things. Mars 360, because this is a natural inclination influenced by Mars, makes laws so that this archetype is allowed some expression of this lack of regard and is also legally protected from societal backlash. This is applied if the person identifies himself as a Mars-1(taking the mark essentially) This applies across the board for all six sectors.

Mars 360 equates to 666 using English Sumerian Gematria where each letter is numbered in multiples of 6 (A=6, B=12, C=18, D=24, etc). The word "Mars" adds up to 306. Add "360" to 306 and you get 666. I then used this to call down fire from heaven in accordance and in fulfillment of Revelation 13:13. The evidence and documentation of this is laid out in the book entitled "The Deus Armaaruss" 3rd edition and many of my other works.

Now the mark of the beast implementation, as well as making the image of Armaaruss has a cohesive and actionable vision via Artificial Intelligence which has laid out and organized the basic infrastructure.

Cet article aborde la question de la prolifération des systèmes d’intelligence artificielle (IA) tant dans le domaine civil que militaire. Il discute du besoin éventuel d’une réglementation spécifique au niveau international pour encadrer l’usage des systèmes d’IA génératives (en anglais « General Purpose AI » (ci-après GPAI). Il présente les défis posés par les systèmes GPAI et les efforts de réglementation en cours. Du fait des possibilités de double usage des systèmes GPAI, ce papier soulève la question d'un encadrement normatif pour faciliter le contrôle de ces systèmes au niveau international. Dans ce contexte, il propose une réflexion comparée avec le droit international nucléaire.

Эта книга — справочник по использованию искусственного интеллекта для решения юридических задач. От основ работы ИИ до практических техник составления запросов и примеров промптов для юристов — вы найдете здесь пошаговые инструкции и рекомендации, которые сделают взаимодействие с искусственным интеллектом простым и эффективным. Развивайте свои навыки и открывайте новые горизонты в профессии с помощью искусственного интеллекта!

This paper argues that equitable legal reasoning is not an exclusive property of human judges. To investigate whether judges' perceptions can be regarded as a fundamental and special prerequisite for equitable reasoning, I discuss the virtue jurisprudence thesis. Then, using the aporetic logic of deconstructive justice, I outline a framework for the prospective content of equitable reasoning for human judges and future artificial intelligence (AI) judges. However, despite the optimistic and visionary claims about AI judges' ability to make decisions in a similar way to human judges, pervasive skepticism exists. This essay critically analyzes this skepticism by focusing on the possibility of programing algorithmic models to encode values associated with Aristotle's concept of epikeia. To demonstrate how algorithmic reasoning can be harmonized with value-based principles, a case-based algorithmic model is presented as a representative example. The goal of this example is to challenge the assumption that human judges have an inherent advantage in (equitable) legal reasoning. To illustrate that the reasoning of human judges is not always equitable, I present an example of an inequitable decision by the Turkish Supreme Court of Appeals. These examples serve to justify my two claims. First, there is no compelling reason to favor the 'virtuous' and 'legal' reasoning of human judges over the 'algorithmic' reasoning of AI judges. Second, the legal responsibility to consider the particular nuances of each case-the central element of equitable reasoning-can be seen as a quality attributed to both human and AI judges.

Machine readable, actionable rules represent a precondition for developing systems endowed with automatic reasoning facilities for advanced information services in the legal domain. In this talk we present an approach for legal knowledge representation and reasoning within a Linked Open Data framework. It is based on the distinction between provisions and norms and it is able to provide advanced reasoning facilities for advanced legal information retrieval (like implementing Hohfedian reasoning) and legal compliance checking for deontic notions. It is also shown how the approach can handle norm defeasibility. Such methodology is implemented by decidable fragments of OWL 2, while legal reasoning is implemented by available decidable reasoners. Moreover, an approach based on machine learning and natural language processing is presented, able to bridge the gap between knowledge modeling and knowledge acquisition.

I t is not a coincidence that the research fields of artificial intelligence and of law have met and have merged into the interdisciplinary research field of artificial intelligence and law. Both fields use formal methods, with their strengths and limitations; in AI there are software, logic, and statistics, in law there are statutes, procedures, and institutions. Both fields are creative; in AI, systems are built, experiments designed, and paradigms replaced; in law, regulations are passed by lawmakers, precedents are set, and ideologies balanced. Both fields struggle with the inevitable complexity of modeling human behavior-in AI with the goal to reconstruct human behavior, in law with the goal to steer human behavior. These and other similarities are driving the active and dedicated community of AI and law. Researchers are taking their inspiration from the law with its insights developed over millennia combining them with AI's half a century of lessons. The beginnings of AI and law are marked by the

Cracking the Code: Rulemaking for Humans and Machines is an OECD Working Paper authored<br> by James Mohun and Alex Roberts and released on 14 October 2020. This Working Paper on<br> Public Governance, produced by the OECD Observatory of Public Sector Innovation, is available<br> in English and French. A draft version was publicly available on the web for comments. In August<br> 2020 the La Trobe LawTech team reached out to the authors and provided some comments on the<br> draft Working Paper. Rules as Code is gaining momentum, increasing the need for constructive<br> critical engagement. To support the broader discussions around Rules as Code, we are publishing<br> our August 2020 Comments. These are provided in their original form, with small technical and<br> language edits for wider publication.

This study explores the dynamic intersection of Artificial Intelligence (AI) and legal education, highlighting the imperative for curriculum reform and the integration of interdisciplinary studies. It underscores the transformative impact of AI and technological innovations on legal practices and educational frameworks, necessitating a paradigm shift in how legal education is approached.
The introduction delves into the historical evolution of legal education alongside early AI influences, emphasizing how AI is reshaping the landscape of legal education. It discusses the integration of AI in teaching methodologies and the benefits and challenges it poses, including the need for balance, ethical considerations, and quality assurance in legal education.
The study examines how AI is revolutionizing legal research, enhancing personalization in legal education, and introducing simulation-based learning. It delves into the integration of AI in the legal curriculum, impacting legal theory, analysis, and practical skills development while also exploring the ethical and jurisprudential implications of AI.
The document discusses the accessibility and diversity challenges in AI-influenced legal education, analyzing the financial aspects and the impact of AI on affordability and sustainability. It also addresses the gap between academic training and practical skills, highlighting the evolving legal markets and future skills required in legal education influenced by AI.
Legal informatics' relationship with AI is explored, emphasizing its application in legal education and practice. The benefits, challenges, and potential solutions in implementing legal informatics in legal education are discussed, focusing on ethics, transparency, and accessibility.
This book presents a comprehensive compilation of 30 university curricula courses and the most influential books on AI and Law.
The study concludes by looking at the future of legal education with AI at its core. It discusses how AI and online platforms are revolutionizing legal learning and teaching methods, the challenges and benefits involved, and the necessity for reform and innovation in legal education curricula to integrate AI effectively.

Keywords: Legal Education, Artificial Intelligence (AI), Legal Informatics,      Curriculum Reform, Interdisciplinary Studies, Legal Practice, Technology in Law, AI in Legal Research, Legal Ethics, Online Legal Education, Legal Technology, Simulation-Based Learning, Data Privacy, Cybersecurity Law, Intellectual Property, AI Legal Applications, Future Legal Skills, Legal Curriculum Innovation, AI and Legal Theory, Digital Transformation in Law.

This paper analyses and compares some of the automated reasoners that have been used in recent research for compliance checking. Although the list of the considered reasoners is not exhaustive, we believe that our analysis is representative enough to take stock of the current state of the art in the topic. We are interested here in formalizations at the first-order level. Past literature on normative reasoning mostly focuses on the propositional level. However, the propositional level is of little usefulness for concrete LegalTech applications, in which compliance checking must be enforced on (large) sets of individuals. Furthermore, we are interested in technologies that are freely available and that can be further investigated and compared by the scientific community. In other words, this paper does not consider technologies only employed in industry and/or whose source code is non-accessible. This paper formalizes a selected use case in the considered reasoners and compares the i...

One of the main issues of every business process is to be compliant with legal rules. This work presents a methodology to check in a semi-automated way the regulatory compliance of a business process. We analyse an e-Health hospital service in particular: the Hospital at Home (HaH) service. The paper shows, at first, the analysis of the hospital business using the Business Process Management and Notation (BPMN) standard language, then, the formalization in Defeasible Deontic Logic (DDL) of some rules of the European General Data Protection Regulation (GDPR). The aim is to show how to combine a set of tasks of a business with a set of rules to be compliant with, using a tool.

ceedings before national authorities. On the other hand, the imprecise language of the GDPR makes it susceptible to interpretations deeply embedded in the hitherto elaborated practices of the Polish administrative procedure. The article analyses the intersection between the GDPR and Polish administrative procedure in order to examine the potential for mutual influences between both frameworks.

Cracking the Code: Rulemaking for Humans and Machines is an OECD Working Paper authored<br> by James Mohun and Alex Roberts and released on 14 October 2020. This Working Paper on<br> Public Governance, produced by the OECD Observatory of Public Sector Innovation, is available<br> in English and French. A draft version was publicly available on the web for comments. In August<br> 2020 the La Trobe LawTech team reached out to the authors and provided some comments on the<br> draft Working Paper. Rules as Code is gaining momentum, increasing the need for constructive<br> critical engagement. To support the broader discussions around Rules as Code, we are publishing<br> our August 2020 Comments. These are provided in their original form, with small technical and<br> language edits for wider publication.

Knowledge representation and reasoning in the legal domain has primarily focused on case studies where knowledge and data can fit in main memory. However, this assumption no longer applies in the era of big data, where large amounts of data are generated daily. This paper discusses new opportunities and challenges that emerge in relation to reasoning with legal big data and the concepts of volume, velocity, variety and veracity. A four-layer legal big data framework is proposed to manage the complete lifecycle of legal big data from sourcing, processing and storage, to reasoning, analysis and consumption. Within each layer, a number of relevant future research directions are also identified, which can facilitate the realisation of knowledge-rich legal big data solutions.

This paper presents an approach for legal compliance checking in the Semantic Web which can be effectively applied for applications in the Linked Open Data environment. It is based on modeling deontic norms in terms of ontology classes and ontology property restrictions. It is also shown how this approach can handle norm defeasibility. Such methodology is implemented by decidable fragments of OWL 2, while legal reasoning is carried out by available decidable reasoners. The approach is generalised by presenting patterns for modeling deontic norms and norms compliance checking.

This paper reports on a case study in which the use of the Rationale software was investigated to analyse the argumentation structure of a Dutch expert witness report in a criminal case. The underlying motivation of the case study was to explore the usefulness of argumentation visualisation software for increasing a judge’s understanding of expert reports and for assisting him or her in asking the proper critical questions to the expert. By way of an initial exploration of this usefulness, an expert report was analysed with the Rationale software. The visualisation was informally discussed with a legal expert, who was generally positive but also expressed some concerns and expected that the main usefulness of the tool is in training and education of judges.

ceedings before national authorities. On the other hand, the imprecise language of the GDPR makes it susceptible to interpretations deeply embedded in the hitherto elaborated practices of the Polish administrative procedure. The article analyses the intersection between the GDPR and Polish administrative procedure in order to examine the potential for mutual influences between both frameworks.

This paper argues that if we want a sustainable environment of desirable AI systems, we should aim not only at transparent, explainable, fair, lawful, and accountable algorithms, but we also should seek for “just” algorithms, that is, automated decision-making systems that include all the above-mentioned qualities (transparency, explainability, fairness, lawfulness, and accountability). This is possible through a practical “justification” statement and process (eventually derived from algorithmic impact assessment) through which the data controller proves, in practical ways, why the AI system is not unfair, not discriminatory, not obscure, not unlawful, etc. In other words, this justification (eventually derived from data protection impact assessment on the AI system) proves the legality of the system with respect to all data protection principles (fairness, lawfulness, transparency, purpose limitation, data minimisation, accuracy, storage limitation, integrity, and accountability)....

Urban centres around the world are currently experiencing an unforeseen set of challenges. These are derived from an increasing demand for basic services, which include a more efficient management of financial and technical resources. In this sense, an area that is currently looking for new strategies to solve this problem is Latin America, being Mexico City one of the most important cases. The Mexican capital is one of the most populated cities in the world, currently holding large levels of cultural, economic and social disparities. To solve this, urban planners, computer developers and social experts have developed a solution known as “smart cities”. This concept operates through the combination of intelligent technology, urban planning and citizen collaboration. Nonetheless, this requires a new model of governance that allows the interaction among the parties to be not only efficient but secure and law compliant. In the current paper, this scenario will be delivered from the per...

The goal of the present research is to define a Semantic Web framework for precedent modelling, by using knowledge extracted from text, metadata, and rules, while maintaining a strong text-to-knowledge morphism between legal text and legal concepts, in order to fill the gap between legal document and its semantics. The framework is composed of four different models that make use of standard languages from the Semantic Web stack of technologies: a document metadata structure, modelling the main parts of a judgement, and creating a bridge between a text and its semantic annotations of legal concepts; a legal core ontology, modelling abstract legal concepts and institutions contained in a rule of law; a legal domain ontology, modelling the main legal concepts in a specific domain concerned by case-law; an argumentation system, modelling the structure of argumentation. The input to the framework includes metadata associated with judicial concepts, and an ontology library representing th...

This research aims to define an integrated methodology for modelling judgments, starting from legal texts and capturing not only structural parts but also arguments used by judges to reach conclusions. The goal is to build a complete ontology framework capable of detecting and modelling knowledge from the judgement’s text. The formalized judgements provide the necessary metadata for the rule layer to enable argumentation towards the acceptance or rejection of a given interpretation. For pursuing this goal it is important to integrate the legal ontology construction with a rule formalization following legal reasoning-oriented theory and defeasible logics, just like the Carneades application (presented here) follows Walton's argumentation theory. The XML interchange uses OWL, RuleML, and the emerging LegalRuleML.

This research aims to define an integrated methodology for modelling judgments, starting from legal texts and capturing not only structural parts but also arguments used by judges to reach conclusions. The goal is to build a complete ontology framework capable of detecting and modelling knowledge from the judgement's text. The formalized judgements provide the necessary metadata for the rule layer to enable argumentation towards the acceptance or rejection of a given interpretation. For pursuing this goal it is important to integrate the legal ontology construction with a rule formalization following legal reasoning-oriented theory and defeasible logics, just like the Carneades application (presented here) follows Walton's argumentation theory. The XML interchange uses OWL, RuleML, and the emerging LegalRuleML.

Cracking the Code: Rulemaking for Humans and Machines is an OECD Working Paper authored<br> by James Mohun and Alex Roberts and released on 14 October 2020. This Working Paper on<br> Public Governance, produced by the OECD Observatory of Public Sector Innovation, is available<br> in English and French. A draft version was publicly available on the web for comments. In August<br> 2020 the La Trobe LawTech team reached out to the authors and provided some comments on the<br> draft Working Paper. Rules as Code is gaining momentum, increasing the need for constructive<br> critical engagement. To support the broader discussions around Rules as Code, we are publishing<br> our August 2020 Comments. These are provided in their original form, with small technical and<br> language edits for wider publication.

Traditionally, computational knowledge representation and reasoning focused its attention on rich domains such as the law. The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing range of scientific disciplines, as well as business and human activities, are becoming data-driven. This chapter summarises existing research on legal representation and reasoning in order to uncover technical challenges associated both with the integration of rules and databases and with the main concepts of the big data landscape. We expect these challenges lead naturally to future research directions towards achieving large scale legal reasoning with rules and databases.

Urban centres around the world are currently experiencing an unforeseen set of challenges. These are derived from an increasing demand for basic services, which include a more efficient management of financial and technical resources. In this sense, an area that is currently looking for new strategies to solve this problem is Latin America, being Mexico City one of the most important cases. The Mexican capital is one of the most populated cities in the world, currently holding large levels of cultural, economic and social disparities. To solve this, urban planners, computer developers and social experts have developed a solution known as “smart cities”. This concept operates through the combination of intelligent technology, urban planning and citizen collaboration. Nonetheless, this requires a new model of governance that allows the interaction among the parties to be not only efficient but secure and law compliant. In the current paper, this scenario will be delivered from the per...

El análisis de precedentes dentro del dominio jurídico es de amplio impacto y valor, las sentencias emitidas en los diferentes niveles jurisprudenciales pueden tener una vinculación y un posible sesgo involuntario de juicio. El administrar el conocimiento explícito y las fuentes de los máximos tribunales, puede habilitar en obtener de manera anticipada un alertamiento por proximidad de contenido, utilizando diversas técnicas de inteligencia artificial. En esta investigación se presenta un caso de uso práctico de análisis de ejecutorias y cómo estas tienen una representación por análisis de texto con aproximación lingüística, y presentar un análisis con procesamiento de lenguaje natural, utilizando el lenguaje R.

In the domain of Linked Open Data a need is emerging for developing automated frameworks able to generate the licensing terms associated to data coming from heterogeneous distributed sources. This paper proposes and evaluates a deontic logic semantics which allows us to define the deontic components of the licenses, i.e., permissions, obligations, and prohibitions, and generate a composite license compliant with the licensing items of the composed different licenses. Some heuristics are proposed to support the data publisher in choosing the licenses composition strategy which better suits her needs w.r.t. the data she is publishing.

Artificial Intelligence (AI) refers to systems designed by humans, interpreting the already collected data and deciding the best action to take, according to the pre-defined parameters, in order to achieve the given goal. Designing, trial and error while using AI, brought ethics to the center of the dialogue between tech giants, enterprises, academic institutions as well as policymakers. Ethical challenges in AI brought ethical AI framework in place in an attempt to regulate people's lives and interactions, used for the benefit of society, for the human rights' protection as well as for the respect of individual's privacy and autonomy. The paper aims to summarize and critically evaluate the basic principles for the use of AI, with emphasis to the General Data Protection Regulation's (GDPR) approach, concerning data subject's consent, data protection principles and data subject's rights in a context of 'privacy by design' architecture. INDEX TERMS AI, privacy, data protection, ethics, GDPR.

The GDPR (GDPR, REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)) introduces the self-assessment of digital risks and the modulation of duties on the basis of the impact assessment analysis, including specific measures that intend to safeguard the data subject's human dignity and fundamental rights. Semantic web technologies and legal reasoning tools can support privacy-by-default and legal compliance. In this light, this paper presents a first draft of a legal ontology on the GDPR, called PrOnto, that has the goal of providing a legal knowledge modelling of the privacy agents, data types, types of processing operations, rights and obligations. The methodology used here is based on legal theory analysis joined with ontological patterns.

Artificial Intelligence (AI) refers to systems designed by humans, interpreting the already collected data and deciding the best action to take, according to the pre-defined parameters, in order to achieve the given goal. Designing, trial and error while using AI, brought ethics to the center of the dialogue between tech giants, enterprises, academic institutions as well as policymakers. Ethical challenges in AI brought ethical AI framework in place in an attempt to regulate people's lives and interactions, used for the benefit of society, for the human rights' protection as well as for the respect of individual's privacy and autonomy. The paper aims to summarize and critically evaluate the basic principles for the use of AI, with emphasis to the General Data Protection Regulation's (GDPR) approach, concerning data subject's consent, data protection principles and data subject's rights in a context of 'privacy by design' architecture. INDEX TERMS AI, privacy, data protection, ethics, GDPR.

Artificial Intelligence (AI) refers to systems designed by humans, interpreting the already collected data and deciding the best action to take, according to the pre-defined parameters, in order to achieve the given goal. Designing, trial and error while using AI, brought ethics to the center of the dialogue between tech giants, enterprises, academic institutions as well as policymakers. Ethical challenges in AI brought ethical AI framework in place in an attempt to regulate people's lives and interactions, used for the benefit of society, for the human rights' protection as well as for the respect of individual's privacy and autonomy. The paper aims to summarize and critically evaluate the basic principles for the use of AI, with emphasis to the General Data Protection Regulation's (GDPR) approach, concerning data subject's consent, data protection principles and data subject's rights in a context of 'privacy by design' architecture. INDEX TERMS AI, privacy, data protection, ethics, GDPR.

This study introduces a descriptive analysis to carry out the transformation of the Dubai smart city as a case study in the GCC region with reference to the Barcelona smart city. Furthermore, to investigate how the Dubai smart city will deal with the huge amount of the collected personal data through Internet of Things devices and applications. The theoretical analysis shows that the Barcelona smart city can be represented as an effective model, its innovations recommends to be used in Dubai smart city. The analysis founds that the classification of the collected data inside smart city to open and shared data did not provide sufficient privacy for personal data. Therefore, the personal data should be classified explicitly in order to be processed separately under the rules of the data protection law.

Artificial Intelligence (AI) refers to systems designed by humans, interpreting the already collected data and deciding the best action to take, according to the pre-defined parameters, in order to achieve the given goal. Designing, trial and error while using AI, brought ethics to the center of the dialogue between tech giants, enterprises, academic institutions as well as policymakers. Ethical challenges in AI brought ethical AI framework in place in an attempt to regulate people's lives and interactions, used for the benefit of society, for the human rights' protection as well as for the respect of individual's privacy and autonomy. The paper aims to summarize and critically evaluate the basic principles for the use of AI, with emphasis to the General Data Protection Regulation's (GDPR) approach, concerning data subject's consent, data protection principles and data subject's rights in a context of 'privacy by design' architecture. INDEX TERMS AI, privacy, data protection, ethics, GDPR.

Artificial intelligence is a widely debated topic, but it is often poorly defined. The purpose of this paper will be to return to the foundations of its functioning: mathematical and statistical formalism. While it works successfully in closed, quantifiable environments, it is far less adapted to open environments like the justice system. Indeed, the use of case law with these methods does not make it possible to enhance the judicial memory faithfully as the judgments that are processed do not necessarily include all of the grounds for a decision. This critique must not obscure another reality, which cuts across our whole society: an emerging project of governance by data, which is likely to call into question the very foundations of our democracies, including the rule of law.