Towards Consumer-Empowering Artificial Intelligence

Artificial Intelligence and Law is undergoing a critical transformation. Traditionally focused on the development of expert systems and on a scholarly effort to develop theories and methods for knowledge representation and reasoning in the legal domain, this discipline is now adapting to a sudden change of scenery. No longer confined to the walls of academia, it has welcomed new actors, such as businesses and companies, who are willing to play a major role and seize new opportunities offered by the same transformational impact that recent AI breakthroughs are having on many other areas. As it happens, commercial interests create new opportunities but they also represent a potential threat to consumers, as the balance of power seems increasingly determined by the availability of data. We believe that while this transformation is still in progress, time is ripe for the next frontier of this field of study, where a new shift of balance may be enabled by tools and services that can be of service not only to businesses but also to consumers and, more generally, the civil society. We call that frontier consumer-empowering AI

An empirical analysis of Brazilian courts law documents using learning techniques

This paper describes a survey on investigating judicial data to find patterns and relations between crime attributes and corresponding decisions made by courts, aiming to find import directions that interpretation of the law might be taking. We have developed an initial methodology and experimentation to look for behaviour patterns to build judicial sentences in the scope of Brazilian criminal courts and achieved results related to important trends in decision making. Neural networks-based techniques were applied for classification and pattern recognition, based on Multi-Layer Perceptron and Radial-basis Functions, associated with data organisation techniques and behavioral modalities extractio

Theoretical Concepts of Consumer Resilience to Online Privacy Violation

Resilience is a multifaceted concept used to explain both system and individual behavior across disciplines. Although definitions and research concepts of resilience vary significantly, resilience has become a boundary object in diverse academic fields calling for a holistic approach. This work aims to elaborate the theoretical concepts that might be applied in the research of consumer resilience to online privacy violation, a new and unexplored aspect of consumer behavior in the digital environment. The purpose of the research is to develop the future research frontiers in investigating consumer resilience to online privacy violation. It contributes to the privacy resilience debate and lays the groundwork for developing a conceptual model of online consumer resilience that would explore how individual behavior is affected after online privacy violation occurrence. Developing a conceptual model of consumer resilience to online privacy violation that would include a set of individual and environmental variables, will contribute to the existing understanding of resilience at the intersection of psychology, economics, and privacy studies. Furthermore, it will also contribute to the understanding of adaptive responses of resilient individuals to privacy breaches in an online environment, as well as to the understanding of processes by which resilience affects adaptive responses of consumers in the specific context of online privacy breaches

Judicial Intelligent Assistant System: Extracting Events from Divorce Cases to Detect Disputes for the Judge

In formal procedure of civil cases, the textual materials provided by different parties describe the development process of the cases. It is a difficult but necessary task to extract the key information for the cases from these textual materials and to clarify the dispute focus of related parties. Currently, officers read the materials manually and use methods, such as keyword searching and regular matching, to get the target information. These approaches are time-consuming and heavily depending on prior knowledge and carefulness of the officers. To assist the officers to enhance working efficiency and accuracy, we propose an approach to detect disputes from divorce cases based on a two-round-labeling event extracting technique in this paper. We implement the Judicial Intelligent Assistant (JIA) system according to the proposed approach to 1) automatically extract focus events from divorce case materials, 2) align events by identifying co-reference among them, and 3) detect conflicts among events brought by the plaintiff and the defendant. With the JIA system, it is convenient for judges to determine the disputed issues. Experimental results demonstrate that the proposed approach and system can obtain the focus of cases and detect conflicts more effectively and efficiently comparing with existing method.Comment: 20 page

Profilowanie na podstawie danych osobowych konsumentów przetwarzanych przez pojazd autonomiczny

Profilowanie w coraz większym stopniu wkracza w życie konsumentów, a równolegle z nim postępuje zjawisko gromadzenia i wykorzystywania danych konsumentów pochodzących z różnych źródeł, w tym przetwarzanych przez autonomiczne pojazdy. Niniejszy artykuł ma na celu scharakteryzowanie kluczowych zagrożeń dla konsumentów, które mogą wynikać z procesu profilowania w oparciu o dane osobowe pozyskane z pojazdów autonomicznych, ze szczególnym uwzględnieniem praktyki personalizacji. W tym kontekście szerzej omówione zostaną dwa akty prawne – ogólne rozporządzenie o ochronie danych, na którego podstawie scharakteryzowany zostanie proces pozyskiwania danych osobowych z pojazdów autonomicznych oraz dyrektywa 2005/29/WE o nieuczciwych praktykach handlowych, w ramach której przeanalizowana zostanie możliwość zakwalifikowania personalizacji reklam oraz wykorzystywania profilowania do optymalizacji warunków umownych jako nieuczciwych praktyk handlowych

Theoretical concepts of consumer resilience to online privacy violation

Resilience is a multifaceted concept used to explain both system and individual behavior across disciplines. Although definitions and research concepts of resilience vary significantly, resilience has become a boundary object in diverse academic fields calling for a holistic approach. This work aims to elaborate the theoretical concepts that might be applied in the research of consumer resilience to online privacy violation, a new and unexplored aspect of consumer behavior in the digital environment. It contributes to the privacy resilience debate and lays the groundwork for developing a conceptual model of online consumer resilience that would explore how individual behavior is affected after online privacy violation occurrence

Theoretical concepts of consumer resilience to online privacy violation

Resilience is a multifaceted concept used to explain both system and individual behavior across disciplines. Although definitions and research concepts of resilience vary significantly, resilience has become a boundary object in diverse academic fields calling for a holistic approach. This work aims to elaborate the theoretical concepts that might be applied in the research of consumer resilience to online privacy violation, a new and unexplored aspect of consumer behavior in the digital environment. It contributes to the privacy resilience debate and lays the groundwork for developing a conceptual model of online consumer resilience that would explore how individual behavior is affected after online privacy violation occurrence

Studio della Polarizzazione delle Opinioni nei Social Network attraverso tecniche di Intelligenza Artificiale

Con l'avvento della tecnologia moderna, basata principalmente sull'Intelligenza Artificiale, la quantità di informazioni disponibili è aumentata in modo esponenziale rispetto al passato.Tra i vari inconvenienti, possiamo anche identificare la non affidabilità di molte fonti di informazione. I Social Network hanno dato un forte impulso alla condivisione: la libertà di pensiero e l'eterogeneità dei contenuti espressi hanno portato ad avere risorse diverse sullo stesso argomento, senza che queste vengano approfonditamente verificate. In questo lavoro, analizziamo alcune metriche proposte in letteratura per la misurazione del fenomeno della polarizzazione dell'opinione e, come caso di studio, applichiamo queste metriche a un set di dati contenente i risultati di un sondaggio americano che ha raccolto le opinioni degli utenti su varie questioni sociali. L'applicazione delle varie metriche ha permesso di identificare un certo grado di polarizzazione nel dataset che può essere mappato sui due principali allineamenti politici americani. Infine, data la correlazione tra le varie variabili coinvolte nell'analisi, abbiamo sviluppato una rete neurale in grado di predire l'allineamento politico dell'utente con una precisione molto elevata

Artificial Intelligence Inventions & Patent Disclosure

Artificial intelligence (“AI”) has attracted significant attention and has imposed challenges for society. Yet surprisingly, scholars have paid little attention to the impediments AI imposes on patent law’s disclosure function from the lenses of theory and policy. Patents are conditioned on inventors describing their inventions, but the inner workings and the use of AI in the inventive process are not properly understood or are largely unknown. The lack of transparency of the parameters of the AI inventive process or the use of AI makes it difficult to enable a future use of AI to achieve the same end state. While patent law’s enablement doctrine focuses on the particular result of the invention process, in contrast, this Article suggests that AI presents a lack of transparency and difficulty in replication that profoundly and fundamentally challenge disclosure theory in patent law. A reasonable onlooker or a patent examiner may find it difficult to explain the inner workings of AI. But even more pressing is a non-detection problem—an overall lack of disclosure of unidentified AI inventions, or knowing whether the particular end state was produced by the use of AI. The complexities of AI require enhancing the disclosure requirement since the peculiar characteristics of the end state cannot be described by the inventive process that produced it. This Article introduces a taxonomy of AI and argues that an enhanced AI patent disclosure requirement mitigates concerns surrounding the explainability of AI-based tools and the inherent inscrutability of AI-generated output. Such emphasis of patent disclosure for AI may steer some inventors toward trade secrecy and push others to seek patent protection against would-be patent infringers despite added ex ante costs and efforts. Utilitarian and Lockean theories suggest justifications for enhanced AI patent disclosure while recognizing some objections. Turning to the prescriptive, this Article proposes and assesses, as means for achieving enhanced disclosure, a variety of disclosure-specific incentives and data deposits for AI. It concludes by offering insights for innovation and for a future empirical study to verify its theoretical underpinnings