Data, Privacy, and Surveillance

Summary

Data, privacy, and surveillance are closely intertwined dimensions of contemporary digital societies. Surveillance has long relied on the collection, classification, and analysis of data about individuals and populations for purposes of governance, security, and economic activity. Through practices such as censuses, demographic measurement, statistical analysis, and administrative record-keeping, states and institutions have sought to make populations visible and governable. Contemporary digital technologies have expanded these capacities by enabling the continuous collection, storage, and processing of data generated through everyday activities, transforming personal information into a critical social, political, and economic resource. The growing importance of data has been accompanied by processes of datafication, whereby social activities, behaviors, and interactions are converted into digital information that can be stored, analyzed, and reused across multiple contexts. The value of data lies not simply in its collection but in its capacity to be (re)used, (re)combined, repurposed, and transformed into new forms of knowledge. As a result, contemporary surveillance operates not through direct observation alone but through systems designed to generate predictions, classifications, and behavioral insights from large-scale data analysis. The expansion of artificial intelligence has intensified surveillance by enabling machine-learning systems to infer sensitive characteristics such as health conditions, political preferences, consumer habits, and pregnancy status from population-level data, even when individuals have not directly disclosed such information. These developments expose the limits of traditional privacy frameworks often organized around individual control, consent, and the protection of personal information since contemporary data systems generate knowledge through relationships among datasets and populations rather than through information provided by a single individual. Privacy is therefore better understood as a collective and relational condition. The infrastructures that support data collection, processing, and analysis are controlled by a small number of technology firms whose services are used by governments and public institutions, raising concerns about accountability, technological dependency, digital sovereignty, and control over the infrastructures through which data is transformed into knowledge and used to govern social, economic, and political life.