261 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

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Challenges and perspectives of hate speech research

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    This book is the result of a conference that could not take place. It is a collection of 26 texts that address and discuss the latest developments in international hate speech research from a wide range of disciplinary perspectives. This includes case studies from Brazil, Lebanon, Poland, Nigeria, and India, theoretical introductions to the concepts of hate speech, dangerous speech, incivility, toxicity, extreme speech, and dark participation, as well as reflections on methodological challenges such as scraping, annotation, datafication, implicity, explainability, and machine learning. As such, it provides a much-needed forum for cross-national and cross-disciplinary conversations in what is currently a very vibrant field of research

    Geographic information extraction from texts

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    A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    Technology Assessment of Dual-Use ICTs - How to Assess Diffusion, Governance and Design

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    Technologies that can be used in military and civilian applications are referred to as dual-use. The dual-use nature of many information and communications technologies (ICTs) raises new questions for research and development for national, international, and human security. Measures to deal with the risks associated with the various dual-use technologies, including proliferation control, design approaches, and policy measures, vary widely. For example, Autonomous Weapon Systems (AWS) have not yet been regulated, while cryptographic products are subject to export and import controls. Innovations in artificial intelligence (AI), robotics, cybersecurity, and automated analysis of publicly available data raise new questions about their respective dual-use risks. Dual-use risks have been systematically discussed so far, especially in the life sciences, which have contributed to the development of methods for assessment and risk management. Dual-use risks arise, among other things, from the fact that safety-critical technologies can be easily disseminated or modified, as well as used as part of a weapon system. Therefore, the development and adaptation of robots and software requires an independent consideration that builds on the insights of related dual-use discourses. Therefore, this dissertation considers the management of such risks in terms of the proliferation, regulation, and design of individual dual-use information technologies. Technology Assessment (TA) is the epistemological framework for this work, bringing together the concepts and approaches of Critical Security Studies (CSS) and Human-Computer Interaction (HCI) to help evaluate and shape dual-use technologies. In order to identify the diffusion of dual-use at an early stage, the dissertation first examines the diffusion of dual-use innovations between civilian and military research in expert networks on LinkedIn, as well as on the basis of AI patents in a patent network. The results show low diffusion and tend to confirm existing studies on diffusion in patent networks. In the following section, the regulation of dual-use technologies is examined in the paper through two case studies. The first study uses a discourse analysis to show the value conflicts with regard to the regulation of autonomous weapons systems using the concept of Meaningful Human Control (MHC), while a second study, as a long-term comparative case study, analyzes the change and consequences of the regulation of strong cryptography in the U.S. as well as the programs of intelligence agencies for mass surveillance. Both cases point to the central role of private companies, both in the production of AWS and as intermediaries for the dissemination of encryption, as well as surveillance intermediaries. Subsequently, the dissertation examines the design of a dual-use technology using an Open Source Intelligence System (OSINT) for cybersecurity. For this purpose, conceptual, empirical, and technical studies are conducted as part of the Value-Sensitive Design (VSD) framework. During the studies, implications for research on and design of OSINT were identified. For example, the representative survey of the German population has shown that transparency of use while reducing mistrust is associated with higher acceptance of such systems. Additionally, it has been shown that data sparsity through the use of expert networks has many positive effects, not only improving the performance of the system, but is also preferable for legal and social reasons. Thus, the work contributes to the understanding of specific dual-use risks of AI, the regulation of AWS and cryptography, and the design of OSINT in cybersecurity. By combining concepts from CSS and participatory design methods in HCI, this work provides an interdisciplinary and multi-method contribution

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
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