Notes on Big Data Surveillance in Policing

The intersection of two developments in the past decade:
Growth of surveillance practices.
Rise of big data analytics across various fields (finance, health, social science, etc.).
The article by Sarah Brayne focuses on policing, particularly within the Los Angeles Police Department (LAPD).

Big Data is defined by four key features:
Vast: Involves large amounts of information (often in petabytes).
Fast: Requires high-frequency observations and quick data processing.
Disparate: Merges data from varied sources and sensors.
Digital: Facilitated by the digitization of records, making data easier to analyze and share.

Surveillance practices date back to at least the sixteenth century.
The 9/11 attacks heightened the demand for surveillance and data collection to prevent terrorism.

Traditional policing was reactive and often ineffective.
Shifted towards more proactive approaches like hot spots policing and CompStat (a management model using statistical data for policing operations).
Introduction of predictive policing which utilizes analytics to forecast crimes rather than reactive investigations.

Discretionary Assessments to Risk Scores: Use of numeric risk scores supplements officers' judgments to quantify risk.
Predictive Over Reactive Purposes: Data analytics is increasingly used for prediction rather than just responding to crime.
Automated Alerts for Surveillance: Systems that issue alerts enable monitoring of many individuals without extensive human intervention.
Lower Threshold for Database Inclusion: Expanded databases now include individuals without direct police contact, broadening surveillance reach.
Integration of Data Systems: Previously separate systems are integrated, allowing for cross-referencing between various data sources across institutions.

Social Inequality: Enhanced surveillance practices can reinforce existing biases and deepen inequalities, particularly impacting marginalized communities.
For example, Function Creep: Data collected for one purpose can be used for another, often unintended, purpose, spreading the influence of criminal justice surveillance into other sectors (healthcare, finance, etc.).
- System Avoidance: Individuals with prior police contact may avoid institutions due to fear of surveillance.

Current privacy laws lag behind technological advancements in surveillance.
The fusion of big data with law enforcement complicates the legal landscape concerning personal privacy and surveillance practices.
Calls for heightened legal scrutiny of data-driven policing to protect against potential abuses.

Big data surveillance reveals deep social and institutional dynamics impacting social control and inequality.
There is a need for further examination of how big data practices function across various fields, encompassing both potential advantages and the reinforcement of existing inequalities.