1,268 research outputs found
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
Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals
General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/
Understanding and Mitigating Privacy Vulnerabilities in Deep Learning
Advancements in Deep Learning (DL) have enabled leveraging large-scale datasets to train models that perform challenging tasks at a level that mimics human intelligence. In several real-world scenarios, the data used for training, the trained model, and the data used for inference can be private and distributed across multiple distrusting parties, posing a challenge for training and inference. Several privacy-preserving training and inference frameworks have been developed to address this challenge. For instance, frameworks like federated learning and split learning have been proposed to train a model collaboratively on distributed data without explicitly sharing the private data to protect training data privacy. To protect model privacy during inference, the model owners have adopted a client-server architecture to provide inference services, wherein the end-users are only allowed black-box access to the model’s predictions for their input queries.
The goal of this thesis is to provide a better understanding of the privacy properties of the DL frameworks used for privacy-preserving training and inference. While these frameworks have the appearance of keeping the data and model private, the information exchanged during training/inference has the potential to compromise the privacy of the parties involved by leaking sensitive data. We aim to understand if these frameworks are truly capable of preventing the leakage of model and training data in realistic settings. In this pursuit, we discover new vulnerabilities that can be exploited to design powerful attacks that can overcome the limitations of prior works and break the illusion of privacy. Our findings highlight the limitations of these frameworks and underscore the importance of principled techniques to protect privacy. Furthermore, we leverage our improved understanding to design better defenses that can significantly deter the efficacy of an attack.Ph.D
XAIR: A Framework of Explainable AI in Augmented Reality
Explainable AI (XAI) has established itself as an important component of
AI-driven interactive systems. With Augmented Reality (AR) becoming more
integrated in daily lives, the role of XAI also becomes essential in AR because
end-users will frequently interact with intelligent services. However, it is
unclear how to design effective XAI experiences for AR. We propose XAIR, a
design framework that addresses "when", "what", and "how" to provide
explanations of AI output in AR. The framework was based on a
multi-disciplinary literature review of XAI and HCI research, a large-scale
survey probing 500+ end-users' preferences for AR-based explanations, and three
workshops with 12 experts collecting their insights about XAI design in AR.
XAIR's utility and effectiveness was verified via a study with 10 designers and
another study with 12 end-users. XAIR can provide guidelines for designers,
inspiring them to identify new design opportunities and achieve effective XAI
designs in AR.Comment: Proceedings of the 2023 CHI Conference on Human Factors in Computing
System
Chatbots for Modelling, Modelling of Chatbots
Tesis Doctoral inédita leÃda en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de IngenierÃa Informática. Fecha de Lectura: 28-03-202
Automatic Generation of Personalized Recommendations in eCoaching
Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio
Künstliche Intelligenz in der Hochschulbildung. Chancen und Grenzen des KI-gestützten Lernens und Lehrens
Der Einsatz von Künstlicher Intelligenz erfährt in hochschuldidaktischen Kontexten wachsendes Interesse. Durch individualisierte Lernwege ergeben sich für Studierende und Dozierende neue Lern-Lehr-Möglichkeiten. Der Band befasst sich jedoch auch mit den daraus resultierenden Gefahren: Es sind Demarkationslinien zu ziehen, wenn es um Datenschutz oder ethische Bedenken geht. Die Autor*innen des Bandes nehmen verschiedene Sichtweisen ein, um eine multidisziplinäre Betrachtung der neuen Problemstellungen, die die Anwendung von KI im Hochschulkontext mit sich bringt, zu ermöglichen. (DIPF/Orig.)Higher education teaching contexts are becoming increasingly interested in the use of artificial intelligence. Individualized learning paths give students and lecturers new opportunities for learning and teaching. The volume, however, also covers the risks that follow: There must be clear boundaries when it comes to privacy or ethical issues. To enable a multidisciplinary view of the new issues raised by the application of AI in the university context, the volume\u27s authors adopt various vantage points. (Editors
Intelligent interface agents for biometric applications
This thesis investigates the benefits of applying the intelligent agent paradigm to biometric identity verification systems. Multimodal biometric systems, despite their additional complexity, hold the promise of providing a higher degree of accuracy and robustness. Multimodal biometric systems are examined in this work leading to the design and implementation of a novel distributed multi-modal identity verification system based on an intelligent agent framework. User interface design issues are also important in the domain of biometric systems and present an exceptional opportunity for employing adaptive interface agents. Through the use of such interface agents, system performance may be improved, leading to an increase in recognition rates over a non-adaptive system while producing a more robust and agreeable user experience. The investigation of such adaptive systems has been a focus of the work reported in this thesis.
The research presented in this thesis is divided into two main parts. Firstly, the design, development and testing of a novel distributed multi-modal authentication system employing intelligent agents is presented. The second part details design and implementation of an adaptive interface layer based on interface agent technology and demonstrates its integration with a commercial fingerprint recognition system. The performance of these systems is then evaluated using databases of biometric samples gathered during the research.
The results obtained from the experimental evaluation of the multi-modal system demonstrated a clear improvement in the accuracy of the system compared to a unimodal biometric approach. The adoption of the intelligent agent architecture at the interface level resulted in a system where false reject rates were reduced when compared to a system that did not employ an intelligent interface. The results obtained from both systems clearly express the benefits of combining an intelligent agent framework with a biometric system to provide a more robust and flexible application
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Social Addictive Gameful Engineering (SAGE): A Game-based Learning and Assessment System for Computational Thinking
At an unrivaled and enduring pace, computing has transformed the world, resulting in demand for a universal fourth foundation beyond reading, writing, and arithmetic: computational thinking (CT). Despite increasingly widespread acceptance of CT as a crucial competency for all, transforming education systems accordingly has proven complex. The principal hypothesis of this thesis is that we can improve the efficiency and efficacy of teaching and learning CT by building gameful learning and assessment systems on top of block-based programming environments. Additionally, we believe this can be accomplished at scale and cost conducive to accelerating CT dissemination for all.
After introducing the requirements, approach, and architecture, we present a solution named Gameful Direct Instruction. This involves embedding Parsons Programming Puzzles (PPPs) in Scratch, which is a block-based programming environment currently used prevalently in grades 6-8. PPPs encourage students to practice CT by assembling into correct order sets of mixed-up blocks that comprise samples of well-written code which focus on individual concepts. The structure provided by PPPs enable instructors to design games that steer learner attention toward targeted learning goals through puzzle-solving play. Learners receive continuous automated feedback as they attempt to arrange programming constructs in correct order, leading to more efficient comprehension of core CT concepts than they might otherwise attain through less structured Scratch assignments. We measure this efficiency first via a pilot study conducted after the initial integration of PPPs with Scratch, and second after the addition of scaffolding enhancements in a study involving a larger adult general population.
We complement Gameful Direct Instruction with a solution named Gameful Constructionism. This involves integrating with Scratch implicit assessment functionality that facilitates constructionist video game (CVG) design and play. CVGs enable learner to explore CT using construction tools sufficiently expressive for personally meaningful gameplay. Instructors are enabled to guide learning by defining game objectives useful for implicit assessment, while affording learners the opportunity to take ownership of the experience and progress through the sequence of interest and motivation toward sustained engagement. When strategically arranged within a learning progression after PPP gameplay produces evidence of efficient comprehension, CVGs amplify the impact of direct instruction by providing the sculpted context in which learners can apply CT concepts more freely, thereby broadening and deepening understanding, and improving learning efficacy. We measure this efficacy in a study of the general adult population.
Since these approaches leverage low fidelity yet motivating gameful techniques, they facilitate the development of learning content at scale and cost supportive of widespread CT uptake. We conclude this thesis with a glance at future work that anticipates further progress in scalability via a solution named Gameful Intelligent Tutoring. This involves augmenting Scratch with Intelligent Tutoring System (ITS) functionality that offers across-activity next-game recommendations, and within-activity just-in-time and on-demand hints. Since these data-driven methods operate without requiring knowledge engineering for each game designed, the instructor can evolve her role from one focused on knowledge transfer to one centered on supporting learning through the design of educational experiences, and we can accelerate the dissemination of CT at scale and reasonable cost while also advancing toward continuously differentiated instruction for each learner
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