807 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    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

    Unique Experiences:Designing Warm Technology to Support Personal Dynamics in Dementia

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    Reshaping the Museum of Zoology in Rome by Visual Storytelling and Interactive Iconography

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    This article summarizes the concept of a new immersive and interactive setting for the Zoology Museum in Rome, Italy. The concept, co-designed with all the museum’s curators, is aimed at enhancing the experiential involvement of the visitors by visual storytelling and interactive iconography. Thanks to immersive and interactive technologies designed by Centro Studi Logos, developed by Logosnet and known as e-REALâ and MirrorMeä, zoological findings and memoirs come to life and interact directly with the visitors in order to deepen their understanding, visualize stories and live experiences, and interact with the founder of the Museum (Mr. Arrigoni degli Oddi) who is now a virtualized avatar, or digital human, able to talk with the visitors. All the interactions are powered through simple hand gestures and, in a few cases, vocal inputs that transform into recognized commands from multimedia systems

    Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals

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    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/

    Peer Priming? A Large-Scale Field Experiment Studying the Impact of Popular Rankings on Demand in Mobile Retail

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    Consumers on mobile retail apps face significant search costs due to the small screen size of devices. One of the search aid features to improve the search convenience is to show consumers a small set of frequently used searches conducted by peer consumers on the platform as a prime cue. We refer to this feature as the popular ranking search aid (PRSA). Collaborating with Meituan, a leading services mobile app in China, we implement a large-scale field experiment to explore how PRSA affects consumer search activities and purchases. Our analyses generate three key findings. First, PRSA leads to an increase of 18.6% in page views and a 6.4% increase in purchases. Second, the change in shopping behavior emerges through a change in search behavior with more non-directed searches and fewer directed searches. Third, our mediation analysis supports that search behavior mediates the business outcomes. We offer theoretical and managerial implications

    Active Curation: algorithmic awareness for cultural commentary on social media platforms

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    This thesis examines how everyday social media users engage in curation practices to influence what news and information they see on their social feeds. It finds that cultural commentary content can act as a proxy for news on these platforms, contributing to public debate and the fifth estate. While much research has explored the implications of algorithmically driven recommender systems for content personalisation and news visibility, this thesis investigates a gap in our understanding of how social media users understand and respond to algorithmic processes, customising their feed in their day-to-day curation practices on these platforms. It explores how a group of Australians aged 18–30 respond to algorithmic recommender systems and how effective their practices are in shaping their social feeds. The study used a mixed methods approach that included a digital ethnography of social media use and a comparative content analysis of social media news exposure and topics in the legacy news cycle. This study develops a taxonomy of consumptive curation practices that users can engage in to influence their personalised social feeds. The study also examines users’ motivations for this curation and how effective these are in filtering news and ‘cultural commentary’ content into or out of their feed. The findings demonstrate that algorithmic literacy is a driver of active curation practices, where users consciously engage in practices designed to influence recommender processes that customise their social feed. They also demonstrate the prevalence of non-journalistic news-related content or ‘cultural commentary’ on social media platforms in the form of hot takes, memes, and satire, and how this cultural commentary can act as a proxy for the news, even for users who are news avoidant. These findings address gaps in our understanding of news discovery and consumption on social media platforms, with implications for how news businesses can reach emerging news audiences

    Automatic Generation of Personalized Recommendations in eCoaching

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    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

    No Consumer Is an Island—Relational Disclosure as a Regulatory Strategy to Advance Consumer Protection Against Microtargeting

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    Presently, most business-to-consumer interaction uses consumer profiling to elaborate and deliver personalized products and services. It has been observed that these practices can be welfare-enhancing if properly regulated. At the same time, risks related to their abuses are present and significant, and it is no surprise that in recent times, personalization has found itself at the centre of the scholarly and regulatory debate. Within currently existing and forthcoming regulations, a common perspective can be found: given the capacity of microtargeting to potentially undermine consumers’ autonomy, the success of the regulatory intervention depends primarily on people being aware of the personality dimension being targeted. Yet, existing disclosures are based on an individualized format, focusing solely on the relationship between the professional operator and its counterparty; this approach operates in contrast to sociological studies that consider interaction and observation of peers to be essential components of decision making. A consideration of this “relational dimension” of decision making is missing both in consumer protection and in the debate on personalization. This article defends that consumers’ awareness and understanding of personalization and its consequences could be improved significantly if information was to be offered according to a relational format; accordingly, it reports the results of a study conducted in the streaming service market, showing that when information is presented in a relational format, people’s knowledge and awareness about profiling and microtargeting are significantly increased. The article further claims the potential of relational disclosure as a general paradigm for advancing consumer protection
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