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AbstractEmerging technologies permeate and potentially disrupt a wide spectrum of our social, economic, and political relations. Various state institutions, including education, law enforcement, and healthcare, increasingly rely on technical components, such as automated decision-making systems, e-government systems, and other digital tools to provide cheap, efficient public services, and supposedly fair, transparent, disinterested, and accountable public administration. The increased interest in various blockchain-based solutions from central bank digital currencies, via tokenized educational credentials, and distributed ledger-based land registries to self-sovereign identities is the latest, still mostly unwritten chapter in a long history of standardized, objectified, automated, technocratic, and technologized public administration. The rapid, (often) unplanned, and uncontrolled technologization of public services (as happened in the hasty adoption of distance-learning and teleconferencing systems during Corona Virus Disease (COVID) lockdowns) raises complex questions about the use of novel technological components, which may or may not be ultimately adequate for the task for which they are used. The question whether we can trust the technical infrastructures the public sector uses when providing public services is a central concern in an age where trust in government is declining: If the government's artificial intelligence system that detects welfare fraud fails, the public's confidence in the government is ultimately hit. In this paper, we provide a critical assessment of how the use of potentially untrustworthy (private) technological systems including blockchain-based systems in the public sector may affect trust in government. We then propose several policy options to protect the trust in government even if some of their technological components prove fundamentally untrustworthy.

AbstractThough discussions of data protection have focused on the larger, more established organisations, startups also warrant attention. This is particularly so for tech startups, who are often innovating at the 'cutting‐edge'—pushing the boundaries of technologies that typically lack established data protection best‐practices. Initial decisions taken by startups could well have long‐term impacts, and their actions may inform (for better or for worse) how particular technologies and the applications they support are implemented, deployed, and perceived for years to come. Ensuring that the innovations and practices of tech startups are sound, appropriate and acceptable should therefore be a high priority. This paper explores the attitudes and preparedness of tech startups to issues of data protection. We interviewed a series of UK‐based emerging tech startups as the EU's General Data Protection Regulation (GDPR) came into effect, which revealed areas in which there is a disconnect between the approaches of the startups and the nature and requirements of the GDPR. We discuss the misconceptions and associated risks facing innovative tech startups and offer a number of considerations for the firms and supervisory authorities alike. In light of our discussions, and given what is at stake, we argue that more needs to be done to help ensure that emerging technologies and the practices of the companies that operate them better align with the regulatory obligations. We conclude that tech startups warrant increased attention, support, and scrutiny to raise the standard of data protection for the benefit of us all.

The technological infrastructures enabling the collection, processing, and trading of data have fuelled a rapid innovation of data governance models. We differentiate between macro, meso, and micro level models, which correspond to major political blocks; societal-, industry-, or community level systems, and individual approaches, respectively. We focus on meso-level models, which coalesce around: (1) organisations prioritising their own interests over interests of other stakeholders; (2) organisations offering technological and legal tools aiming to empower individuals; (3) community-based data intermediaries fostering collective rights and interests. In this article we assess these meso-level models, and discuss their interaction with the macro-level legal frameworks that have evolved in the US, the EU, and China. The legal landscape has largely remained inconsistent and fragmented, with enforcement struggling to keep up with the latest developments. We argue, first, that the success of meso-logics is largely defined by global economic competition, and, second, that these meso-logics may potentially put the EU's macro-level framework with its mixed internal market and fundamental rights-oriented model under pressure. We conclude that, given the relative absence of a strong macro level-framework and an intensive competition of governance models at meso-level, it may be challenging to avoid compromises to the European macro framework.

The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement n. 615159 (ERC Consolidator Grant DEPRIVEDHOODS, Socio-spatial inequality, deprived neighbourhoods, and neighbourhood effects), as well as from European Union's Horizon 2020 research and innovation programme under Grant Agreement n. 727097 (RELOCAL). ; The non-random selection of people into neighbourhoods complicates the estimation of causal neighbourhood effects on individual outcomes. Measured neighbourhood effects could be the result of characteristics of the neighbourhood context, but they could also result from people selecting into neighbourhoods based on their preferences, income, and the availability of alternative housing. This paper examines how the neighbourhood effect on individual income is altered when geographic selection correction terms are added as controls, and how these results vary across three Dutch urban regions. We use a two-step approach in which we first model neighbourhood selection, and then include neighbourhood choice correction components in a model estimating neighbourhood effects on individual income. Using longitudinal register datasets for three major Dutch cities: Amsterdam, Utrecht and Rotterdam, and multilevel models, we analysed the effects for individuals who moved during a 5-year period. We show that in all cities, the effect of average neighbourhood income on individual income becomes much smaller after controlling for explicitly modelled neighbourhood selection. This suggests that studies that do not control for neighbourhood selection most likely overestimate the size of neighbourhood effects. For all models, the effects of neighbourhood income are strongest in Rotterdam, followed by Amsterdam and Utrecht. ; Publisher PDF ; Peer reviewed

Part 1: Introduction -- Rising inequalities and a changing social geography of cities. An introduction to the global segregation book -- Residential segregation between income groups in international perspective -- Part 2: Africa -- Income inequality, socio-economic status and residential segregation in Greater Cairo: 1986-2006 -- Social inequality and spatial segregation in Cape Town -- Income inequality and socio-economic segregation in the city of Johannesburg -- Part 3: Asia -- Dual land regime, income inequalities and multifaceted socio-economic and spatial segregation in Hong Kong -- Income inequality and socioeconomic segregation in Jakarta -- Socio-spatial segregation and exclusion in Mumbai -- Social polarisation and socio-economic segregation in Shanghai, China: Evidence from 2000 and 2010 censuses -- Increasing inequality and the changing spatial distribution of income in Tel-Aviv -- Changes in occupational structure and residential segregation in Tokyo -- Part 4: Australia -- The land of the 'fair go'? Mapping income inequality and socioeconomic segregation across Melbourne neighbourhoods -- Part 5: Europe -- Making sense of segregation in a well-connected city: the case of Berlin.

Funding: The UK Medical Research Council and Wellcome (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website. This research was specifically funded by the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 615159 (ERC Consolidator Grant DEPRIVEDHOODS, Socio-spatial inequality, deprived neighbourhoods, and neighbourhood effects). ; We studied the relation between cumulative exposure to neighbourhood deprivation and adolescents' Big Five personality traits, and the moderating role of personality in the relation between neighbourhood deprivation and the development of problem behaviour and educational attainment. We studied 5365 British adolescents from ages 10 to 16, with neighbourhood information from birth onwards. Extraversion, agreeableness, emotional stability, and openness to experience moderated the relation between deprivation and problem behaviour. For educational attainment, only extraversion was a moderator. This means that higher values on personality traits were related to weaker relations between neighbourhood deprivation and problem behaviour and educational attainment. The results showed the importance of taking into account adolescents' personality when assessing developmental outcomes in relation to neighbourhood deprivation. ; Publisher PDF ; Peer reviewed

The research leading to these results received funding from the Estonian Research Council [Institutional Research grant numbers PUT PRG306 and IUT2-17, projects Infotechnological Mobility Laboratory and RITA-Ränne]; and the European Research Council under the European Union's Seventh Framework Programme (FP/2007- 2013)/ERC [grant agreement number 615159] (ERC Consolidator Grant DEPRIVEDHOODS, Socio-Spatial Inequality, Deprived Neighbourhoods, and Neighbourhood Effects). ; This paper provides new insights into the relationships between income inequality and residential segregation between socioeconomic groups by undertaking a comparative study of European urban regions. In Europe, income inequalities are the lowest in North Europe and the highest in South Europe. In many East European countries, a switch from low inequality to high inequality has taken place. The main findings show that changes in the levels of residential segregation between socioeconomic groups correlate to changes in the levels of income inequality found approximately 10 years earlier, that is, with a time lag. ; Publisher PDF ; Peer reviewed