Predict and Surveil is one of the first ethnographic studies of data-driven policing in the United States. In this landmark work, American sociologist Sarah Brayne focuses on the Los Angeles PoliceA state institution responsible for maintaining public order, enforcing laws, and preventing crime. Department (LAPD), a pioneer in predictive policing—and a department widely criticized for its surveillance practices. Brayne analyzes how big data, algorithmic predictions, and digital surveillance not only reshape police practices but also reconfigure internal institutional structures, decision-making logics, and power relations within law enforcement organizations.
Traditional Policing vs. Predictive Policing
| Aspect | Traditional Policing | Predictive Policing |
|---|---|---|
| Guiding principle | Reactive (after the crime) | Preventive (before the crime) |
| Focus | Clearance of committed offences | Forecasting potential offences |
| Basis of action | Witnesses, suspects, criminal reports | Data, patterns, algorithms |
| Decision-making | Individual discretion | System-based recommendations |
| Data sources | Police reports, statements, evidence | Big Data (e.g., criminal history, locations, social networks) |
| Criticism | Bias, arbitrariness, lack of resources | Algorithmic bias, lack of transparency, reinforcement of inequality |
| Transparency | Traceable decisions | Opaque models (“black box”) |
| Control logic | Case-based | Risk-based |
Digital Policing as a Field of Research
Brayne’s study is based on several years of participant observation at the LAPD, complemented by interviews with officers and civilian data analysts. The core question is how digital technologies are integrated into everyday police work—and what consequences this integration has for justice, efficiency, and institutional oversight. Unlike many theoretical works on predictive policing, Brayne offers a micro-sociological inside view of an institution in transition. She shows not only what big data enables, but also what it displaces, entrenches, or obscures.
Key Points
Predict and Surveil by Sarah Brayne
Main Proponent: Sarah Brayne
First Published: 2020
Country: United States
Key Concepts: Predictive policing, big data, discretion, data power, algorithmic decision support
Key Thesis: The use of data-driven prediction systems transforms not only police practice but the institutional structure and decision-making logic of law enforcement. Algorithms do not replace discretion—they reframe it, with potentially problematic implications for justice, transparency, and equality.
Theoretical Context: SurveillanceSystematic monitoring of people’s activities, behaviors, or communications. Studies, Harcourt (Against Prediction), Mathiesen (Viewer SocietyA group of individuals connected by shared institutions, culture, and norms.), critical sociology of technology
Core Arguments
Brayne’s central argument is that the implementation of data tools changes not only the techniques of policing but the institution of the police itself. The LAPD is evolving into a “data-generating institution”—an organization that not only processes data but systematically produces, circulates, and utilizes it. These processes shape what is considered relevant information, how suspects are categorized, and how risk is defined.
Brayne also observes that algorithmic tools such as heat maps, risk assessments, and network analyses do not replace officer discretion, but restructure its basis. Data guides decisions in advance—it suggests objectivity but can reproduce or reinforce existing biases and structural inequalities. Particularly troubling is what Brayne calls “asymmetric transparency”: affected individuals have little or no insight into the systems that influence their categorization or targeting.
Theoretical Framework
Brayne situates her work within Surveillance Studies and the critical sociology of technology. She builds on scholars such as Oscar Gandy, David Lyon, danah boyd, and Bernard Harcourt, while offering an empirically grounded and original contribution. Not all uses of data are inherently problematic, she argues—problems arise when data-driven tools interact with unequally distributed resources, rights, and spaces.
Empirical Insights into the LAPD
A major strength of the book lies in its rich empirical detail. Brayne describes specific situations where data systems are deployed—for example, in prioritizing patrol zones, mapping social networks (“gang affiliation”), or assessing recidivism risks. She explores how backstage data work is conducted—often by civilian tech experts whose roles are rarely acknowledged within police self-conceptions. At the same time, Brayne shows that technical systems are neither neutral nor infallible. They rely on assumptions, weightings, and interpretations that are shaped by political and social contexts.
Critique and Criminological Relevance
Predict and Surveil is not merely a case study of American policing. It is a foundational text for critical engagement with digital surveillance in the 21st century. Brayne makes clear that big data in policing is not an objective authority—it is a social product, entangled in normative, institutional, and political dimensions. She warns against “technological determinism”: the notion that technological tools can solve social problems without addressing their root causes.
In criminology, Brayne’s work is a key contribution to debates on digital inequality, automated selection mechanisms, and the future of democratic oversight. It offers a critical supplement to classical theories of social control (e.g., Foucault, Mathiesen) and provides an empirical foundation for discussions on police transparency, ethics, and algorithmic fairness.
Related Key Works
- Bernard Harcourt – Against Prediction (2007)
- Bernard Harcourt – The Illusion of Free Markets (2011)
- Ruha Benjamin – RaceA socially constructed category used to differentiate groups based on perceived physical or cultural traits. After Technology (2019)
- Virginia Eubanks – Automating InequalityUnequal distribution of resources, opportunities, and rights within a society. (2018)
- Thomas Mathiesen – The Viewer Society (1997)
Conclusion
Sarah Brayne’s Predict and Surveil is a groundbreaking contribution to empirical police research and critical surveillance theory. It demonstrates that technological innovation in law enforcement does not automatically lead to fairness, transparency, or efficiency—but may exacerbate existing inequalities. Through precise fieldwork, theoretical depth, and sociopolitical sensitivity, Brayne provides an essential resource for understanding the data-driven police force of the future.
References
- Brayne, S. (2020). Predict and Surveil: Data, Discretion, and the Future of Policing. New York: Oxford University Press.
- Brayne, S. (2017). Big Data Surveillance: The Case of Policing. American Sociological Review, 82(5), 977–1008.
- Ferguson, A. G. (2017). The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement. New York: NYU Press.
- Harcourt, B. E. (2007). Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age. Chicago: University of Chicago Press.
- Personal Website of Sarah Brayne: https://www.sarahbrayne.com/
Video
In this interview, Sarah Brayne discusses the core arguments of Predict and Surveil, reflecting on the use of big data, new forms of algorithmic control, and the societal implications of data-driven policing.
https://youtu.be/3gXMs9GCIh8?feature=shared
