The Covid-19 crisis has served as a watershed moment in this transition. Augmenting the need and societal demand for problem-solving through digital technology, the first pandemic of the datafied society “will be remembered as the ‘great accelerator’ of digital transformation”. Creating novel market prospects, it worked as the ideal testbed for the tech industry to pilot, introduce, or rebrand “new” data-centric technology. Said “innovation under pressure” lowered citizen and policymakers’ concerns over risks and paved the way for concerted privacy trade-offs, prompting observers to lament a “pandemic of surveillance” and the advent of “the age of biometric surveillance”. Digital identity solutions illustrate this acceleration: according to specialized press, 72 percent of online marketplaces have adopted identity verification technology in the first phase of the crisis alone. Governments followed suit: the pandemic created “the right conditions for excuses to be removed” to prompt policymakers to test digital ID systems, as in the case of Canada and of the EU with its plan for European Digital Identity Wallets. But the technologies devised or rolled out amid the crisis are here to stay. “This storm will pass. But the choices we make now could change our lives for years to come”, cautioned historian Yuval Harari in the peak of the pandemic (2020). In the ruins of Covid-19, this project sets off to examine how the post-pandemic society (re)negotiates societal norms and dynamics around governance and regulatory data infrastructure.
Sovereignty
Sovereignty describes the political relationship between a state and its citizens. That relationship comes under strain when the infrastructure governing a population is designed, owned, and maintained by someone else. Tech companies like Palantir and Amazon Web Services have recently come under crossfire for enabling the United States Immigration and Customs Enforcement to track undocumented immigrants, including children, for deportation. These “sovereignty experiments” via data technology “not only concern people, data, information and technologies, but the concept of sovereignty itself”.
Connecting state authority to collective self-rule, sovereignty denotes the political relationship between the state and its people. Today, the centrality of digital infrastructure puts national and individual sovereignty under strain. And technology typically races ahead of policy and regulation. Bellanova et al. (2022) aptly distinguish between “sovereignty over” and “sovereignty through” the digital, i.e. the use of digital tools for governance. Part of the first rubric are EU efforts to reclaim digital sovereignty—think of GAIA-X, a would-be federated secured data infrastructure—but also Russia’s “sovereign Internet” as a novel terrain exercise sovereignty, and the state’s comeback in internet governance.
The question of sovereignty is of interest to DATAGOV for it has to do with statecraft, and the relation state-citizens and state-market. While most research fails to take a systemic view on how distinct levels (governmental, industry, citizenry) interact with one ano
DATAGOV examines emerging assemblages of sovereignty through and over data, following three tensions. The first is state and market: governments depend on the tech industry to deliver the systems they need, but delegating regulatory functions to private infrastructure amounts to a de facto privatisation of public power, and tech companies are not bound to democratic scrutiny. The second is technocratic expertise and values: as decision-making shifts toward computer scientists and engineers, and techno-solutionism gains ground, what happens to politics that is openly value-laden? The third is the national and the supranational: a globalised industry and cross-border coordination requirements press against the nation state as the basic unit of governance. Rather than taking the state for granted, this work traces how power circulates between government, industry, and citizenry, and asks what justifies the expansion of technocratic decision-making.
Inequality
Large segments of the population experience inadequate access to employment, housing, education, nutrition, and healthcare. Structural inequalities capture these durable asymmetries along lines of class, race, geography, or legal status. We therefore advance the concept of infrastructural inequalities. We define infrastructural inequalities the forms of discrimination, exclusion, and oppression that originate from and depend on infrastructural arrangements. Infrastructural inequalities direct attention to a distinct analytical layer: how disadvantage is captured, then organized, consequently stabilized and, we argue, reproduced (and even produced) through data infrastructures themselves.
Algorithmic racism or discrimination captures discriminatory outcomes generated by automated systems that disadvantage racialised groups, often because of biased datasets or entrenched social hierarchies. Similarly, technological redlining highlights how data-driven systems opaquely reproduce social discrimination, drawing on the historical practice in which banks used neighborhood demographics —particularly race and ethnicity— to deny loans, rather than individual creditworthiness. Data poverty refers to systematic absences in datasets that render individuals or communities invisible to algorithmic systems, and thus unable to make rights claims. We argue that, however important, these concepts remain narrowly set on specific, sectorial dynamics of data-mediated classification and decision-making, failing to capture the systemic embedding of these dynamics in contemporary governance arrangements.
Inequality is not merely automated, it is embedded in cables, pipes, interfaces, databases, legal mandates, and institutional arrangements. For example, data centers exert extensive influence over digital activity while remaining spatially and politically distant from the communities they affect. Moreover, they often operate as opaque systems with limited public accountability. Thus, control over data storage and processing is concentrated in a small number of states and corporations, reinforcing contemporary forms of data colonialism – which is to say, how global data extraction parallels historical colonial exploitation. In this sense, infrastructural inequalities are not reducible to structural inequalities alone.
The contemporary shift toward data-driven governance has the potential to reproduce structural inequalities by infrastructure, and to produce new forms of inequality specific to the characteristics of these infrastructures. This thematic area within DATAGOV extends the line of inquiry concerned with algorithmic discrimination, to examine regulatory data infrastructures as assemblages, and by grounding infrastructural inequalities through comparative analysis.
Citizenship
In April 2023, France adopted a legislation allowing the use of real-time, AI-powered surveillance cameras for the 2024 Paris Summer Olympics. “If anybody can be reported to the police for allegedly ‘anomalous’ behavior, a chilling effect is to be expected’, warned a coalition of EU lawmakers lamenting that governance “should not be left to algorithms”.
Citizenship—the ability to participate in public life—has evolved under the pressure of digital technology. Digital citizenship is “self-constructed and self-defined, but equally constructed by the governmental and business realms”. This limits users’ agency and capacity to shape decisions that govern their lives, as do human and machine interaction enumerating and modeling human behavior. Following feminist approaches to radical democracy, DATAGOV identifies citizenship with the practices through which individuals reclaim and enact their agency. Agency, the sociological process of “making sense of the world so as to act within in”, is to be constructed beyond the “juridical demands upon the state” and towards the “quotidian politics (…) of social practices”. It implicates an individual’s relation with the state and with society as a whole.
This moment is a unique testbed for probing the evolution of citizenship vis-à-vis the growing role of non-human actants making decisions—think of the penetration of generative AI in society. Although research has shown that datafication alters the exercise of citizenship (e.g., Dencik et al., 2018), to date little is known about how lay persons make sense of, organize, and resist data infrastructure designed to discipline their life for societal purposes, like facial recognition. DATAGOV tests the impact of governance by data infrastructure on citizenship by exploring subject and agency formation within invasive data regimes, as well as the emergence of collective action.