The Data Class

This book studies algorithmic power by examining surveillance in Singapore. Drawing on critical theorists, it introduces the 'authorised domain': an elite status group with exclusive access to surveillance algorithms and data. This group maintains power through data 'mobilisation' and 'demobilisation'. The Singapore case study demonstrates how automated law enforcement (ALE) and predictive analytics may be used to normalize skewed data realities, reinforcing social inequalities and reshaping class and power in the digital age. The author suggests that algorithmic power is primarily exercised by an 'authorised domain'—a technocratic actors acting as a status group—which leverages exclusive access to surveillance systems and data algorithms for political and economic advantage. Drawing on Foucault, Deleuze, and Weber, the study uses Singapore as a case to consider how a group sustains power by managing data flows through mobilisation (selectively introducing information) and demobilisation (selectively withholding information). These practices enable automated law enforcement (ALE), automated decision making systems, and predictive analytics to reproduce existing social inequalities, may disproportionately affect vulnerable groups and ultimately forging a new data class protected from surveillance and equipped to shape the algorithmic environment. The primary audience for this book is clearly defined as scholars and students across a range of interdisciplinary fields, specifically including communications theory, sociology, surveillance studies, criminology, political science, and data governance, with a particular focus on graduate and PhD students who will engage with the theoretical depth. The secondary audience consists of policy analysts, technologists, and legal scholars interested in the practical applications and socio-political implications of automated surveillance systems, privacy, AI and ethics, as they are concerned with the real-world impact and regulation of the concepts the book explores.