5,334 research outputs found

    Public Service Media as Enablers of Epistemic Rights

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    Growing concerns in recent years over threats to the foundations of democratic societies posed by misinformation, hate speech and other problems associated with digital communications have led to renewed calls for greater protection of epistemic rights within policy, advocacy and academic fora. Institutionally mandated to promote citizenship, public service media (PSM) organisations have an important role to play in supporting epistemic rights. We suggest four main conditions are required for PSM to fulfil this role. First, PSM are premised upon strong political commitment. At a time when this political commitment is dwindling, it is imperative that civil society, academia and international organisations continue to make the case for PSM strong. Second, we argue that PSM need to evolve with the times, and be allowed to use new transmission means, build new platforms and innovate. Third, we argue that PSM need to move beyond supporting epistemic rights, as they have traditionally been bestowed, and work to promote epistemic justice, by questioning the existing power structures of knowledge. Finally, PSM need to work together with other educational and cultural institutions towards the creation of an epistemic commons, countering the privatisation of communitive spaces and striving to make knowledge accessible to all

    Conceptual Approach to Managing the Development of Creative Industries in Second-Tier Industrial Cities

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    Relevance. In contemporary urban development, knowledge and creativity play pivotal roles in transforming cities into instruments for increased attractiveness, economic growth, and citizen well-being. Despite this recognition, second-tier cities face challenges due to the lack of a comprehensive concept for managing creative industries. The creative economy, proven effective in various countries, holds potential solutions to address accumulated issues.Research objective. This study aims to conceptualize the management of the creative sector in second-tier industrial cities, fostering their revitalization and transformation into growth zones.Data and methods. The empirical focus includes 14 creative clusters in old industrial second-tier cities of the Urals and Siberia in Russia. The study constructs a typology delineating five types of creative cluster formation, based on public-private sector interaction. Content analysis examines research literature and is complemented by a systematic approach.Results. The article systematizes the most pivotal problems in the management of creative industries' development and shows the importance of addressing institutional and coordination issues. Types of creative cluster formation are identified based on the degree of interaction between the public and private sectors in the development of creative industries. Two key types of creative cluster formation—initiative-based and dependent—are identified through case studies. The study formulates a conceptual approach to managing creative industries in second-tier industrial cities.Conclusions. The management of creative industries in second-tier cities deserves to be acknowledged as a distinct area of management. Development of creative industries requires a systematic state support system and a well-defined strategy. Local authorities play a crucial role in this process, acting as focal points for cooperative efforts through regulatory innovations in urban creative industries. Key instruments for effective policy implementation include the transformation of urban spaces, establishment of creative clusters, provision of grants to support small businesses, and stimulation of export activities

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    E-learning in the Cloud Computing Environment: Features, Architecture, Challenges and Solutions

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    The need to constantly and consistently improve the quality and quantity of the educational system is essential. E-learning has emerged from the rapid cycle of change and the expansion of new technologies. Advances in information technology have increased network bandwidth, data access speed, and reduced data storage costs. In recent years, the implementation of cloud computing in educational settings has garnered the interest of major companies, leading to substantial investments in this area. Cloud computing improves engineering education by providing an environment that can be accessed from anywhere and allowing access to educational resources on demand. Cloud computing is a term used to describe the provision of hosting services on the Internet. It is predicted to be the next generation of information technology architecture and offers great potential to enhance productivity and reduce costs. Cloud service providers offer their processing and memory resources to users. By paying for the use of these resources, users can access them for their calculations and processing anytime and anywhere. Cloud computing provides the ability to increase productivity, save information technology resources, and enhance computing power, converting processing power into a tool with constant access capabilities. The use of cloud computing in a system that supports remote education has its own set of characteristics and requires a unique strategy. Students can access a wide variety of instructional engineering materials at any time and from any location, thanks to cloud computing. Additionally, they can share their materials with other community members. The use of cloud computing in e-learning offers several advantages, such as unlimited computing resources, high scalability, and reduced costs associated with e-learning. An improvement in the quality of teaching and learning is achieved through the use of flexible cloud computing, which offers a variety of resources for educators and students. In light of this, the current research presents cloud computing technology as a suitable and superior option for e-learning systems

    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

    Bruno Latour and Artificial Intelligence

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    This scenario discusses generative AI in light of Bruno Latour’s sociology of technology. It considers why Latour showed little interest in the simulation of intelligence and how connectionist AI fails to meet his condition for scientificity but offers a fascinating writing mediation. AI is most interesting not because it emulates human thinking or writing, but because it differs from them. Drawing on actor-network theory, this scenario argues against the idea of machines becoming detached from their creators and highlights how AIs can only exist through the support of their human assistants. The risks associated with these technologies do not come from an improbable singularity, but from their embedding in the dull and exploitative industry of digital attention economy

    Implementing precision methods in personalizing psychological therapies: barriers and possible ways forward

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    This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: No data was used for the research described in the article.Highlights: • Personalizing psychological treatments means to customize treatment for individuals to enhance outcomes. • The application of precision methods to clinical psychology has led to data-driven psychological therapies. • Applying data-informed psychological therapies involves clinical, technical, statistical, and contextual aspects
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