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The chapter critically analyzes the concepts and the practices of surveillance in modern and postmodern societies along with their consequences. We show the changes in the systems, which are used to monitor individuals, and emphasize the transition toward soft surveillance systems, probably stimulated by digital technologies. This switch from top-down control to “lateral” monitoring systems encloses surveillance practices with suggestive names like interveillance, synopticon, and dataveillance. The dark side of digital health has a bright start. According to Topol’s (2016) vision of the future, we will soon be the “consumers,” the real protagonists, of the management of our health – thanks largely to the practically endless data about our bodies, behaviors, and lifestyles we will be able to collect and analyze. We will share our health information in real time with the doctors whom we will choose based on their score in clinical rankings (here, too, quantification rears its head). Yet, this simplified version of health makes it seem that there are always some solutions, which the algorithm can supply as long as it has enough information. Moreover, in the United States, some health-insurance companies have started to offer a discount on premiums to the members who agree to collect and share self-tracking data with them. Clearly, the discount is given only to the workers who have healthy habits. At first sight, this can seem as a win-win trade-off; however, what today is presented as an individual option can easily become a requirement tomorrow.

In June 2017, The Human Data Commons Foundation released its first annual Quantified Self Report Card. This project consisted of a qualitative review of the privacy policy documentation of 55 private sector companies in the self-tracking and biometric data industry. Two researchers recorded their ratings on concrete criteria for each company’s website, as well as providing a blend of objective and subjective ratings on the overall ease of readability and navigability within each site’s documentation. This chapter explains the unique context of user privacy rights within the Quantified Self tracking industry, and summarises the overall results from the 2017 Quantified Self Report Card. The tension between user privacy and data sharing in commercial data-collection practices is explored and the authors provide insight into possibilities for resolving these tensions. The self-as-instrument in research is touched on in autoethnographic narrative confronting and interrogating the difficult process of immersive qualitative analytics in relation to such intensely complex and personal issues as privacy and ubiquitous dataveillance. Drawing upon excerpted reflections from the Report Card’s co-author, a few concluding thoughts are shared on freedom and choice. Finally, goals for next year’s Quantified Self Report Card are revealed, and a call extended for public participation.

Advances in Big Data, artificial Intelligence and data-driven innovation bring enormous benefits for the overall society and for different sectors. By contrast, their misuse can lead to data workflows bypassing the intent of privacy and data protection law, as well as of ethical mandates. It may be referred to as the ‘creep factor’ of Big Data, and needs to be tackled right away, especially considering that we are moving towards the ‘datafication’ of society, where devices to capture, collect, store and process data are becoming ever-cheaper and faster, whilst the computational power is continuously increasing. If using Big Data in truly anonymisable ways, within an ethically sound and societally focussed framework, is capable of acting as an enabler of sustainable development, using Big Data outside such a framework poses a number of threats, potential hurdles and multiple ethical challenges. Some examples are the impact on privacy caused by new surveillance tools and data gathering techniques, including also group privacy, high-tech profiling, automated decision making and discriminatory practices. In our society, everything can be given a score and critical life changing opportunities are increasingly determined by such scoring systems, often obtained through secret predictive algorithms applied to data to determine who has value. It is therefore essential to guarantee the fairness and accurateness of such scoring systems and that the decisions relying upon them are realised in a legal and ethical manner, avoiding the risk of stigmatisation capable of affecting individuals’ opportunities. Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. These are only some examples of the issues and consequences that Big Data raise, which require adequate measures in response to the ‘data trust deficit’, moving not towards the prohibition of the collection of data but rather towards the identification and prohibition of their misuse and unfair behaviours and treatments, once government and companies have such data. At the same time, the debate should further investigate ‘data altruism’, deepening how the increasing amounts of data in our society can be concretely used for public good and the best implementation modalities.

School districts across the United States have adopted web-based student information systems (SIS) that offer parents, students, teachers and administrators immediate access to a variety of data points on each individual. In this chapter, I offer findings from in-depth interviews with school stakeholders that demonstrates how some students, typically ‘high performers’, are drawn into ‘pushed self-tracking’ (Lupton, 2016) of their academic achievement metrics, obsessively monitoring their grades and other quantified measures through digital devices, comparing their performance to other students and often generating a variety of affective states for themselves. I suggest that an SIS functions as a neoliberal technology of childhood government with these students internalising and displaying the self-governing capacities of ‘enterprise’ and ‘autonomy’ (Rose, 1996). These capacities are a product of and reinforce the metric culture of the school.

Insofar as the digital layer cannot be detached from the current democratic challenges of the 21st century including neoliberalism, scales, civic engagement and action research-driven co-production methodologies; this chapter advances trends, aftermaths and emancipatory strategies for the post-pandemic technopolitical democracies. Consequently, it suggests a democratic toolbox encompassing four intertwined trends, aftermaths and emancipations including (1) the context characterised by the algorithmic nations, (2) challenges stemming from data sovereignty, (3) mobilisation seen from the digital rights perspective and (4) grassroots innovation embodied through data co-operatives. This chapter elucidates that in the absence of coordinated and interdependent strategies to claim digital rights and data sovereignty by algorithmic nations, on the one hand, Big Tech data-opolies, and on the other hand, the GDPR led by the European Commission might bound (negatively) and expand (positively), respectively, algorithmic nations' capacity to mitigate the negative side effects of the algorithmic disruption in Western democracies.

This chapter introduces the book by revolving around its core concept: digital citizenship. This introductory chapter on digital citizenship regimes in the postpandemics could be established by including several brief discussion points that gradually introduce and lead us comprehensively to the chapters of the book previously introduced. These discussion points are informative and attempt to introduce progressively to the key chapters of the book as follows: (1) Urban-Digital Citizenship Nexus; (2) Advancing Recent Literature on Citizenship; (3) Rescaling Nation-States: Pandemic Citizenship and Algorithmic Nations; (4) Beyond the Smart Cities; (5) Exploring Digital Citizenship Towards Technopolitical Dynamics; (6) Borderless and Pandemic Citizenship; and (7) In Summary: Towards Future Research and Policy Avenues in the Postpandemics.

This chapter develops a conceptual taxonomy of five emerging digital citizenship regimes: (1) the globalised and generalisable regime called pandemic citizenship that clarifies how post-COVID-19 datafication processes have amplified the emergence of four intertwined, non-mutually exclusive and non-generalisable new technopoliticalised and city-regionalised digital citizenship regimes in certain European nation-states’ urban areas; (2) algorithmic citizenship, which is driven by blockchain and has allowed the implementation of an e-Residency programme in Tallinn; (3) liquid citizenship, driven by dataism – the deterministic ideology of big data – and contested through claims for digital rights in Barcelona and Amsterdam; (4) metropolitan citizenship, as revindicated in reaction to Brexit and reshuffled through data co-operatives in Cardiff; and (5) stateless citizenship, driven by devolution and reinvigorated through data sovereignty in Barcelona, Glasgow and Bilbao. This chapter challenges the existing interpretation of how these emerging digital citizenship regimes together are ubiquitously rescaling the associated spaces/practices of European nation-states.