Supporting lay users in privacy decisions when sharing sensitive data

The first part of the thesis focuses on assisting users in choosing their privacy settings, by using machine learning to derive the optimal set of privacy settings for the user. In contrast to other work, our approach uses context factors as well as individual factors to provide a personalized set of privacy settings. The second part consists of a set of intelligent user interfaces to assist the users throughout the complete privacy journey, from defining friend groups that allow targeted information sharing; through user interfaces for selecting information recipients, to find possible errors or unusual settings, and to refine them; up to mechanisms to gather in-situ feedback on privacy incidents, and investigating how to use these to improve a user’s privacy in the future. Our studies have shown that including tailoring the privacy settings significantly increases the correctness of the predicted privacy settings; whereas the user interfaces have been shown to significantly decrease the amount of unwanted disclosures.Insbesondere nach den jüngsten Datenschutzskandalen in sozialen Netzwerken wird der Datenschutz für Benutzer immer wichtiger. Obwohl die meisten Benutzer behaupten Wert auf Datenschutz zu legen, verhalten sie sich online allerdings völlig anders: Sie lassen die meisten Datenschutzeinstellungen der online genutzten Dienste, wie z. B. von sozialen Netzwerken oder Diensten zur Standortfreigabe, unberührt und passen sie nicht an ihre Datenschutzanforderungen an. In dieser Arbeit werde ich einen Ansatz zur Lösung dieses Problems vorstellen, der auf zwei verschiedenen Säulen basiert. Der erste Teil konzentriert sich darauf, Benutzer bei der Auswahl ihrer Datenschutzeinstellungen zu unterstützen, indem maschinelles Lernen verwendet wird, um die optimalen Datenschutzeinstellungen für den Benutzer abzuleiten. Im Gegensatz zu anderen Arbeiten verwendet unser Ansatz Kontextfaktoren sowie individuelle Faktoren, um personalisierte Datenschutzeinstellungen zu generieren. Der zweite Teil besteht aus einer Reihe intelligenter Benutzeroberflächen, die die Benutzer in verschiedene Datenschutzszenarien unterstützen. Dies beginnt bei einer Oberfläche zur Definition von Freundesgruppen, die im Anschluss genutzt werden können um einen gezielten Informationsaustausch zu ermöglichen, bspw. in sozialen Netzwerken; über Benutzeroberflächen um die Empfänger von privaten Daten auszuwählen oder mögliche Fehler oder ungewöhnliche Datenschutzeinstellungen zu finden und zu verfeinern; bis hin zu Mechanismen, um In-Situ- Feedback zu Datenschutzverletzungen zum Zeitpunkt ihrer Entstehung zu sammeln und zu untersuchen, wie diese verwendet werden können, um die Privatsphäreeinstellungen eines Benutzers anzupassen. Unsere Studien haben gezeigt, dass die Verwendung von individuellen Faktoren die Korrektheit der vorhergesagten Datenschutzeinstellungen erheblich erhöht. Es hat sich gezeigt, dass die Benutzeroberflächen die Anzahl der Fehler, insbesondere versehentliches Teilen von Daten, erheblich verringern

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Limit order books in statistical arbitrage and anomaly detection

Cette thèse propose des méthodes exploitant la vaste information contenue dans les carnets d’ordres (LOBs). La première partie de cette thèse découvre des inefficacités dans les LOBs qui sont source d’arbitrage statistique pour les traders haute fréquence. Le chapitre 1 développe de nouvelles relations théoriques entre les actions intercotées afin que leurs prix soient exempts d’arbitrage. Toute déviation de prix est capturée par une stratégie novatrice qui est ensuite évaluée dans un nouvel environnement de backtesting permettant l’étude de la latence et de son importance pour les traders haute fréquence. Le chapitre 2 démontre empiriquement l’existence d’arbitrage lead-lag à haute fréquence. Les relations dites lead-lag ont été bien documentées par le passé, mais aucune étude n’a montré leur véritable potentiel économique. Un modèle économétrique original est proposé pour prédire les rendements de l’actif en retard, ce qu’il réalise de manière précise hors échantillon, conduisant à des opportunités d’arbitrage de courte durée. Dans ces deux chapitres, les inefficacités des LOBs découvertes sont démontrées comme étant rentables, fournissant ainsi une meilleure compréhension des activités des traders haute fréquence. La deuxième partie de cette thèse investigue les séquences anormales dans les LOBs. Le chapitre 3 évalue la performance de méthodes d’apprentissage automatique dans la détection d’ordres frauduleux. En raison de la grande quantité de données, les fraudes sont difficilement détectables et peu de cas sont disponibles pour ajuster les modèles de détection. Un nouveau cadre d’apprentissage profond non supervisé est proposé afin de discerner les comportements anormaux du LOB dans ce contexte ardu. Celui-ci est indépendant de l’actif et peut évoluer avec les marchés, offrant alors de meilleures capacités de détection pour les régulateurs financiers.This thesis proposes methods exploiting the vast informational content of limit order books (LOBs). The first part of this thesis discovers LOB inefficiencies that are sources of statistical arbitrage for high-frequency traders. Chapter 1 develops new theoretical relationships between cross-listed stocks, so their prices are arbitrage free. Price deviations are captured by a novel strategy that is then evaluated in a new backtesting environment enabling the study of latency and its importance for high-frequency traders. Chapter 2 empirically demonstrates the existence of lead-lag arbitrage at high-frequency. Lead-lag relationships have been well documented in the past, but no study has shown their true economic potential. An original econometric model is proposed to forecast returns on the lagging asset, and does so accurately out-of-sample, resulting in short-lived arbitrage opportunities. In both chapters, the discovered LOB inefficiencies are shown to be profitable, thus providing a better understanding of high-frequency traders’ activities. The second part of this thesis investigates anomalous patterns in LOBs. Chapter 3 studies the performance of machine learning methods in the detection of fraudulent orders. Because of the large amount of LOB data generated daily, trade frauds are challenging to catch, and very few cases are available to fit detection models. A novel unsupervised deep learning–based framework is proposed to discern abnormal LOB behavior in this difficult context. It is asset independent and can evolve alongside markets, providing better fraud detection capabilities to market regulators

Newman v. Google

3rd amended complain

Constitutional Challenges in the Algorithmic Society

The law struggles to address the constitutional challenges of the algorithmic society. This book is for scholars and lawyers interested in the intersections of law and technology. It addresses the challenges for fundamental rights and democracy, the role of policy and regulation, and the responsibilities of private actors

Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment

Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ‘panopticon’. Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each student’s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy. The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parents’ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ‘data practices’ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ‘cooked’. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy. The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering students’ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools

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

Beyond Quantity: Research with Subsymbolic AI

How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately

Retrying Leopold and Loeb: A Neuropsychological Perspective

They called it the crime of the century; in 1924 in Chicago two brilliant, well-educated, and wealthy young men kidnapped and murdered a 14-year-old boy and killed him for the thrill of it . Expert testimony was presented by several well-known psychiatrists and psychologists, but even with all their clinical insights, none could reach a conclusion about the causal relation between their disturbed childhoods and a violent senseless crime. In fact, the well-known criminal defense attorney Clarence Darrow made little mention of the extensive psychiatric and psychological workups, and the judge did not deal with it in his sentencing. A review of the findings does suggest a delusional disorder for one of the defendants and psychopathy for the other; the interaction of these two disordered personalities led to a perfect storm a confluence of factors that only in combination could result in the brutal crime. Recent developments in neuropsychology allow us to see how these two disordered personalities interacted; the neuropsychological basis of delusional disorder and of psychopathy will be explored in this presentation along with a re-imagined closing argument by their attorney

Tematski zbornik radova međunarodnog značaja. Tom 1 / Međunarodni naučni skup "Dani Arčibalda Rajsa", Beograd, 1-2. mart 2013

The Thematic Conference Proceedings contains 138 papers written by eminent scholars in the field of law, security, criminalistics, police studies, forensics, medicine, as well as members of national security system participating in education of the police, army and other security services from Russia, Ukraine, Belarus, China, Poland, Slovakia, Czech Republic, Hungary, Slovenia, Bosnia and Herzegovina, Montenegro, Republic of Srpska and Serbia. Each paper has been reviewed by two competent international reviewers, and the Thematic Conference Proceedings in whole has been reviewed by five international reviewers. The papers published in the Thematic Conference Proceedings contain the overview of con-temporary trends in the development of police educational system, development of the police and contemporary security, criminalistics and forensics, as well as with the analysis of the rule of law activities in crime suppression, situation and trends in the above-mentioned fields, and suggestions on how to systematically deal with these issues. The Thematic Conference Proceedings represents a significant contribution to the existing fund of scientific and expert knowledge in the field of criminalistic, security, penal and legal theory and practice. Publication of this Conference Proceedings contributes to improving of mutual cooperation between educational, scientific and expert institutions at national, regional and international level