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Cover -- Title Page -- Preface -- Contents -- List of Contributors -- 1. Don't Accept Candy From Strangers: An Analysis of Third-Party Mobile SDKs -- 2. AI and the Right to Explanation: Three Legal Bases under the GDPR -- 3. A Comparison of Data Protection Regulations for Automotive Systems -- 4. Misaligned Union Laws? A Comparative Analysis of Certification in the Cybersecurity Act and the General Data Protection Regulation -- 5. Aggregation, Synthesisation and Anonymisation: A Call for a Risk-based Assessment of Anonymisation Approaches -- 6. The Role of the EU Fundamental Right to Data Protection in an Algorithmic and Big Data World -- 7. Implementing AI in Healthcare: An Ethical and Legal Analysis Based on Case Studies -- 8. Technological Experimentation Without Adequate Safeguards? Interoperable EU Databases and Access to the Multiple Identity Detector by SIRENE Bureaux -- Closing Remarks -- Index -- Copyright Page.

Current big data practices are largely guided by deliberations concerning their efficiency, and optimisation. Yet there is another perspective. This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raise significant ethical issues. Annika Richterich provides a systematic contemporary overview of the field of critical data studies that reflects on – corporate, institutional, and governmental – practices of digital data collection and analysis. It assesses in detail one big data research area: biomedical studies, focused on epidemiological surveillance. Specific case studies explore how big data have been used in academic work. The Big Data Agenda concludes by asking if data ownership can be reclaimed by citizens from being simply an assertion of a conception of rights to (user) data that is defined by technological domination. She argues data literacy and discourse ethics may contain solutions as well as a critique.

Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics, Medical Informatics, Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.

Data have significant potential to address current societal problems not only at the federal and state levels, but also in smaller communities, in neighborhoods, and in the lives of individuals. While the proposition for this potential is that data are and will be shared with and reused by and for communities at different levels, not all data are not systematically or routinely shared for reuse with communities due to social, structural and technical infrastructure barriers. Data intermediary organizations can play a significant role in removing existing barriers while unlocking the potential of data for all, particularly for communities with limited human or financial resources, limited access to existing data infrastructures, and underserved populations. Considering the significance of the data intermediary organizations on local communities, this study aims to explore the role of intermediaries that usually facilitate community members/organizations' data utilization. The findings of this study reveal that data intermediary organizations play four major roles that are crucial in communities' data utilization: (1) democratizing data, (2) adding value to existing data, (3) enhancing communities' data literacy, and (4) building communities' data capacity. This study has several important implications to offer a solution to overcome the challenges of data reuse at the local level. ; Institute for Museum and Library Services