The rise of huge data policing

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Source:   —  October 22, 2017, at 8:04 PM

Clarke School of Law and author of the book The Rise of Large Data Policing: Surveillance, Race, and the Future of Law Enforcement (NYU Press two thousand seventeen).

The rise of huge data policing

Andrew Guthrie Ferguson is Prof of Law at the UDC David A. Clarke School of Law and author of the book The Rise of Huge Data Policing: Surveillance, Race, and the Future of Law Enforcement (NYU Press two thousand seventeen). This article is excerpted with permission of NYU Press from the book.

Surveillance cameras monitor the streets. Rows of networked computers link analysts and police officers to a wealth of law enforcement intelligence.

Real-time crime data comes in. Real-time police deployments go out. This high-tech command middle in downtown LA forecasts the future of policing in America.

Welcome to the LA Police Department’s Real-Time Analysis Critical Response (RACR) Division. The RACR Division, in partnership with Palantir—a private technology company that began developing social network software to track terrorists—has jumped head first into the large data age of policing.

Just as in the hunt for international terror networks, Palantir’s software system integrates, analyzes, and shares otherwise-hidden clues from a multitude of ordinary law enforcement data sources. A detective investigating a robbery suspect types a first title and a physical description into the computer—two fragmented clues that'd have remained paper scraps of unusable data in an earlier era. The database searches for possible suspects.

Age, description, address, tattoos, gang affiliations, vehicle ownership instantly population up in sortable fields. By matching known attributes, the computer narrows the look for to a few choices. A photograph of a possible suspect’s car is identified from an automated license-plate reader scouring the city for data about

the location of every vehicle. Detectives chase up with a witness to identify the car used in the robbery. A match leads to an arrest and a closed case.

Roll call. Monday morning. Patrol officers get digital maps of today’s “crime forecast.” Tiny ruddy boxes signify areas of predicted crime. These boxes represent algorithmic forecasts of heightened criminal activity: years of accumulated crime data crunched by powerful computers to target precise city blocks. Informed by the data, “predictive policing” patrols will give extra attention to these “hot” areas during the shift.

Every day, police wait in the predicted locations looking for the forecast crime. The theory: keep police in the box at the right time and stop a crime. The goal: to deter the criminal actors from victimizing that location.

Soon, real-time facial-recognition software will link existing video surveillance cameras and massive biometric databases to automatically identify people with open warrants. Soon, social media feeds will alert police to imminent violence from rival gangs.

Soon, data-matching technologies will discover suspicious action from billions of otherwise-anonymous consumer transactions and personal communications. By digitizing faces, communications, and patterns, police will instantly and accurately be able to inquire into billions of all-too-human clues.

This is the future. This is the present. This is the beginning of huge data policing.

At the middle of policing’s future is data: crime data, personal data, gang data, associational data, locational data, environmental data, and a growing web of sensor and surveillance sources. This large data arises from the expanded skill to collect, store, sort, and analyze digital clues about crime.

Crime statistics are mined for patterns, and victims of violence are mapped in social networks. While video cameras look our movements, private consumer data brokers map our interests and sell that information to law enforcement.

Phone numbers, emails, and finances can all be studied for suspicious links. Government agencies gather health, educational, and criminal records. Detectives monitor public Facebook, YouTube, and Twitter feeds. Aggregating data centers sort and study the accumulated information in local and federally funded fusion centers.

This is the huge data world of law enforcement—still largely in its infancy but offering vastly more incriminating bits of data to utilize and study.

Photo: Jill Waterman/Photolibrary/Getty Images

 

Behind the data is technology: algorithms, network analysis, data mining, machine learning, and a host of computer technologies being refined and improved every day. Police can identify the Str corner most likely to look the following car theft or the people most likely to be shot.

Prosecutors can target the crime networks most likely to destabilize communities, while analysts can link suspicious behaviors for further investigation. The decisional work of identifying criminal actors, networks, and patterns presently starts with powerful computers crunching large data sets nearly instantaneously. Math provides the muscle to prevent and prosecute crime.

Underneath the data and technology are people—individuals living their lives. Some of these people be engaged in in crime, some not. Some live in poverty, some not. But all presently discover themselves encircled by huge data’s reach. The math behind large data policing targets crime, but in many cities, crime suppression targets communities of color.

Data-driven policing means aggressive police presence, surveillance, and perceived harassment in those communities. Each data point translates to genuine human experience, and many times those experiences stay fraught with all-too-human bias, fear, distrust, and racial tension. For those communities, particularly destitute communities of color, these data-collection efforts cast a shadowy shadow on the future.

These new technologies – innovations that create up the constellation of large data policing – will impact the “who,” “where,” “when,” and “how” we police. A race is on to transmute policing. New developments in consumer data collection have merged with law enforcement’s desire to embrace “smart policing” principles in an effort to expand efficiency amid decreasing budgets. Data-driven technology offers a double win—do more with less resources, and do so in a seemingly objective and neutral manner. […]

The promise of “smarter” law enforcement is unquestionably real, but so is the fear of totalizing surveillance. Growing “law and order” rhetoric can lead to surveillance overreach. Police administrators, advocates, communities, and governments should confront those concerns before — not after — the technology’s implementation.

Photo: Mitchell Funk/Getty Images

 

And society should confront those challenges informed by an understanding of how race has fractured and delegitimized the criminal justice system for many citizens. … People of color, immigrants, religious minorities, the poor, protesters, government critics, and many others who encounter aggressive police surveillance are at increased risk.

But so is everyone, because every one of us produces a detailed data trace that exposes personal details. This data—suctioned up, sold, and surveilled—can be wrong. The algorithmic correlations can be wrong. And if police act on that inaccurate data, lives and liberty can be lost. …

The large data policing revolution has arrived. The singular insight of this innovation is that data-driven predictive technologies can identify and forecast risk for the future. Risk identification is also the goal of this book — to forecast the potential problems of huge data policing as it reshapes law enforcement.

Long-standing tensions surrounding race, secrecy, privacy, power, and freedom are given new life in digital form with the advent of large data analytics. New technologies will open up new opportunities for investigation and surveillance. The technological environment is wealthy with opportunity but also danger.

This book seeks to begin a conversation on the growth of these innovations, with the hope that by exposing and explaining the distorting effects of data-driven policing, society can map for its large data future.

*An excerpt from the book The Rise of Large Data Policing: Surveillance, Race, and the Future of Law Enforcement (two thousand seventeen)

Featured Image: JIM WATSON/AFP/Getty Images

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