Governance of Digitalization in Europe A contribution to the Exploration Shaping Digital Policy - Towards a Fair Digital Society? BertelsmannStiftung Study

Digital policy is a unique policy area. As a cross-cutting policy issue, it has an impact not only on individual areas of regulation but on almost all other policy areas as well. Aspects of digital policy such as data regimes, cybersecurity and standardization issues are relevant not only to the the future of the internet or 5G mobile communications infrastructure, but to other areas of our lives to which they are closely linked, which range from automated driving to digital assistance systems in education and healthcare to the digitalization of sectors such as agriculture and construction. Nevertheless, regulation efforts have thus far been primarily sector-specific and national in their scope. With a few exceptions, such as the EU’s controversial General Data Protection Regulation, there are few digital policy frameworks in place for Europe that defines and integrates basic principles for broad application. Instead, we face a situation in which a variety of approaches stand side by side, at times complementing each other but also – all too often – competing with each other in ways that foster inconsistencies. The development of Europe’s 5G infrastructure is illustrative of this state of affairs. Despite the presence of what were originally uniform objectives across Europe, 28 nationally distinct tendering procedures with different requirements have since emerged. As a result, we must now find ways to manage the problems associated with having three or more networks per country, high costs, a difficult debate over security and the threat of dependency on non-EU providers

End-to-End Privacy for Open Big Data Markets

The idea of an open data market envisions the creation of a data trading model to facilitate exchange of data between different parties in the Internet of Things (IoT) domain. The data collected by IoT products and solutions are expected to be traded in these markets. Data owners will collect data using IoT products and solutions. Data consumers who are interested will negotiate with the data owners to get access to such data. Data captured by IoT products will allow data consumers to further understand the preferences and behaviours of data owners and to generate additional business value using different techniques ranging from waste reduction to personalized service offerings. In open data markets, data consumers will be able to give back part of the additional value generated to the data owners. However, privacy becomes a significant issue when data that can be used to derive extremely personal information is being traded. This paper discusses why privacy matters in the IoT domain in general and especially in open data markets and surveys existing privacy-preserving strategies and design techniques that can be used to facilitate end to end privacy for open data markets. We also highlight some of the major research challenges that need to be address in order to make the vision of open data markets a reality through ensuring the privacy of stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special Issue Cloud Computing and the La

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

Algorithms that Remember: Model Inversion Attacks and Data Protection Law

Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EU's recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance of this area. While the GDPR does apply to the use of models in some limited situations, most of its provisions relate to the governance of personal data, while models have traditionally been seen as intellectual property. We present recent work from the information security literature around `model inversion' and `membership inference' attacks, which indicate that the process of turning training data into machine learned systems is not one-way, and demonstrate how this could lead some models to be legally classified as personal data. Taking this as a probing experiment, we explore the different rights and obligations this would trigger and their utility, and posit future directions for algorithmic governance and regulation.Comment: 15 pages, 1 figur

Tackling the information crisis: a policy framework for media system resilience - the report of the LSE Commission on Truth Trust and Technology

There is a crisis of trust in information. Technology offers unprecedented potential to support informed debate and decision-making, but the threats to reliable information and a healthy public debate are growing. Politicians, regulators, platforms, news media and campaigners are responding, with mixed motives and uncertain results. While most share the view that the information crisis needs addressing, each stakeholder has its own interests. The digital platforms, news organisations, political campaigners and civil society all need to be part of a new integrated policy arrangement. This report seeks to rise above the fray. It outlines the contours of the crisis and some of the evidence for the harm caused. It argues for a bold, structural approach to address what is a systemic problem of a crisis of trust in information

The administrative burden reduction policy boom in Europe: comparing mechanisms of policy diffusion

Much has been written on the diffusion of public management and regulatory reform tools. Available evidence suggests that cross-national policy diffusion is an increasingly significant phenomenon, especially in the European context. While internationalisation of policy discourses and expert communities are regarded as key driving forces of policy diffusion, public management reforms are also said to be particularly vulnerable to mechanisms of 'diffusion without convergence'. This paper analyses the case of policies aiming at reducing administrative burdens of regulations through the lens of the literature on policy diffusion. The diffusion of the so-called Standard Cost Model for measuring administrative burden between 2003 and 2007 is used as a case to explore the mechanisms facilitating policy diffusion in this domain. The analysis reveals patterns of rapid diffusion. This policy boom has been driven by a combination of different mechanisms of policy diffusion rather than by a single driving factor