The first article must highlight and coming up with question. The second article only needs to reading Introduction Big Data Policing and chapter 1 Big data’s watchful eye. Also many highlights and coming up with questions.
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The rise of big data: how it’s changing the way we think about
the world
Authors: Kenneth Cukier and Viktor Mayer-Schoenberger
Date: May-June 2013
From: Foreign Affairs(Vol. 92, Issue 3)
Publisher: Council on Foreign Relations, Inc.
Document Type: Article
Length: 4,978 words
Content Level: (Level 4)
Lexile Measure: 1230L
Full Text:
Everyone knows that the Internet has changed how businesses operate, governments function, and people live. But a new, less
visible technological trend is just as transformative: “big data.” Big data starts with the fact that there is a lot more information floating
around these days than ever before, and it is being put to extraordinary new uses. Big data is distinct from the Internet, although the
Web makes it much easier to collect and share data. Big data is about more than just communication: the idea is that we can learn
from a large body of information things that we could not comprehend when we used only smaller amounts. In the third century BC,
the Library of Alexandria was believed to house the sum of human knowledge. Today, there is enough information in the world to give
every person alive 320 times as much of it as historians think was stored in Alexandria’s entire collection–an estimated 1,200
exabytes’ worth. If all this information were placed on CDs and they were stacked up, the CDs would form five separate piles that
would all reach to the moon. This explosion of data is relatively new. As recently as the year 2000, only one-quarter of all the world’s
stored information was digital. The rest was preserved on paper, film, and other analog media. But because the amount of digital data
expands so quickly–doubling around every three years–that situation was swiftly inverted. Today, less than two percent of all stored
information is nondigital. Given this massive scale, it is tempting to understand big data solely in terms of size. But that would be
misleading. Big data is also characterized by the ability to render into data many aspects of the world that have never been quantified
before; call it “datafication.” For example, location has been datafied, first with the invention of longitude and latitude, and more
recently with GPS satellite systems. Words are treated as data when computers mine centuries’ worth of books. Even friendships and
“likes” are datafied, via Facebook. This kind of data is being put to incredible new uses with the assistance of inexpensive computer
memory, powerful processors, smart algorithms, clever software, and math that borrows from basic statistics. Instead of trying to
“teach” a computer how to do things, such as drive a car or translate between languages, which artificial-intelligence experts have
tried unsuccessfully to do for decades, the new approach is to feed enough data into a computer so that it can infer the probability
that, say, a traffic light is green and not red or that, in a certain context, lumiere is a more appropriate substitute for “light” than leger.
Using great volumes of information in this way requires three profound changes in how we approach data. The first is to collect and
use a lot of data rather than settle for small amounts or samples, as statisticians have done for well over a century. The second is to
shed our preference for highly curated and pristine data and instead accept messiness: in an increasing number of situations, a bit of
inaccuracy can be tolerated, because the benefits of using vastly more data of variable quality outweigh the costs of using smaller
amounts of very exact data. Third, in many instances, we will need to give up our quest to discover the cause of things, in return for
accepting correlations. With big data, instead of trying to understand precisely why an engine breaks down or why a drug’s side effect
disappears, researchers can instead collect and analyze massive quantities of information about such events and everything that is
associated with them, looking for patterns that might help predict future occurrences. Big data helps answer what, not why, and often
that’s good enough. The Internet has reshaped how humanity communicates. Big data is different: it marks a transformation in how
society processes information. In time, big data might change our way of thinking about the world. As we tap ever more data to
understand events and make decisions, we are likely to discover that many aspects of life are probabilistic, rather than certain.
APPROACHING “N=ALL”
For most of history, people have worked with relatively small amounts of data because the tools for collecting, organizing, storing,
and analyzing information were poor. People winnowed the information they relied on to the barest minimum so that they could
examine it more easily. This was the genius of modern-day statistics, which first came to the fore in the late nineteenth century and
enabled society to understand complex realities even when little data existed. Today, the technical environment has shifted 179
degrees. There still is, and always will be, a constraint on how much data we can manage, but it is far less limiting than it used to be
and will become even less so as time goes on. The way people handled the problem of capturing information in the past was through
sampling. When collecting data was costly and processing it was difficult and time consuming, the sample was a savior. Modern
sampling is based on the idea that, within a certain margin of error, one can infer something about the total population from a small
subset, as long the sample is chosen at random. Hence, exit polls on election night query a randomly selected group of several
hundred people to predict the voting behavior of an entire state. For straightforward questions, this process works well. But it falls
apart when we want to drill down into subgroups within the sample. What if a pollster wants to know which candidate single women
under 30 are most likely to vote for? How about university-educated, single Asian American women under 30? Suddenly, the random
sample is largely useless, since there may be only a couple of people with those characteristics in the sample, too few to make a
meaningful assessment of how the entire subpopulation will vote. But if we collect all the data–“n = all,” to use the terminology of
statistics–the problem disappears. This example raises another shortcoming of using some data rather than all of it. In the past, when
people collected only a little data, they often had to decide at the outset what to collect and how it would be used. Today, when we
gather all the data, we do not need to know beforehand what we plan to use it for. Of course, it might not always be possible to collect
all the data, but it is getting much more feasible to capture vastly more of a phenomenon than simply a sample and to aim for all of it.
Big data is a matter not just of creating somewhat larger samples but of harnessing as much of the existing data as possible about
what is being studied. We still need statistics; we just no longer need to rely on small samples. There is a tradeoff to make, however.
When we increase the scale by orders of magnitude, we might have to give up on clean, carefully curated data and tolerate some
messiness. This idea runs counter to how people have tried to work with data for centuries. Yet the obsession with accuracy and
precision is in some ways an artifact of an information-constrained environment. When there was not that much data around,
researchers had to make sure that the figures they bothered to collect were as exact as possible. Tapping vastly more data means
that we can now allow some inaccuracies to slip in (provided the data set is not completely incorrect), in return for benefiting from the
insights that a massive body of data provides. Consider language translation. It might seem obvious that computers would translate
well, since they can store lots of information and retrieve it quickly. But if one were to simply substitute words from a French-English
dictionary, the translation would be atrocious. Language is complex. A breakthrough came in the 1990s, when IBM delved into
statistical machine translation. It fed Canadian parliamentary transcripts in both French and English into a computer and programmed
it to infer which word in one language is the best alternative for another. This process changed the task of translation into a giant
problem of probability and math. But after this initial improvement, progress stalled. Then Google barged in. Instead of using a
relatively small number of high-quality translations, the search giant harnessed more data, but from the less orderly Internet–“data in
the wild,” so to speak. Google inhaled translations from corporate websites, documents in every language from the European Union,
even translations from its giant book-scanning project. Instead of millions of pages of texts, Google analyzed billions. The result is
that its translations are quite good–better than IBM’s were–and cover 65 languages. Large amounts of messy data trumped small
amounts of cleaner data.
FROM CAUSATION TO CORRELATION
These two shifts in how we think about data–from some to all and from clean to messy–give rise to a third change: from causation to
correlation. This represents a move away from always trying to understand the deeper reasons behind how the world works to simply
learning about an association among phenomena and using that to get things done. Of course, knowing the causes behind things is
desirable. The problem is that causes are often extremely hard to figure out, and many times, when we think we have identified them,
it is nothing more than a self-congratulatory illusion. Behavioral economics has shown that humans are conditioned to see causes
even where none exist. So we need to be particularly on guard to prevent our cognitive biases from deluding us; sometimes, we just
have to let the data speak. Take UPS, the delivery company. It places sensors on vehicle parts to identify certain heat or vibrational
patterns that in the past have been associated with failures in those parts. In this way, the company can predict a breakdown before it
happens and replace the part when it is convenient, instead of on the side of the road. The data do not reveal the exact relationship
between the heat or the vibrational patterns and the part’s failure. They do not tell UPS why the part is in trouble. But they reveal
enough for the company to know what to do in the near term and guide its investigation into any underlying problem that might exist
with the part in question or with the vehicle. A similar approach is being used to treat breakdowns of the human machine.
Researchers in Canada are developing a big-data approach to spot infections in premature babies before overt symptoms appear. By
converting 16 vital signs, including heartbeat, blood pressure, respiration, and blood-oxygen levels, into an information flow of more
than 1,000 data points per second, they have been able to find correlations between very minor changes and more serious problems.
Eventually, this technique will enable doctors to act earlier to save lives. Over time, recording these observations might also allow
doctors to understand what actually causes such problems. But when a newborn’s health is at risk, simply knowing that something is
likely to occur can be far more important than understanding exactly why. Medicine provides another good example of why, with big
data, seeing correlations can be enormously valuable, even when the underlying causes remain obscure. In February 2009, Google
created a stir in health-care circles. Researchers at the company published a paper in Nature that showed how it was possible to
track outbreaks of the seasonal flu using nothing more than the archived records of Google searches. Google handles more than a
billion searches in the United States every day and stores them all. The company took the 50 million most commonly searched terms
between 2003 and 2008 and compared them against historical influenza data from the Centers for Disease Control and Prevention.
The idea was to discover whether the incidence of certain searches coincided with outbreaks of the flu–in other words, to see
whether an increase in the frequency of certain Google searches conducted in a particular geographic area correlated with the CDC’s
data on outbreaks of flu there. The CDC tracks actual patient visits to hospitals and clinics across the country, but the information it
releases suffers from a reporting lag of a week or two–an eternity in the case of a pandemic. Google’s system, by contrast, would
work in near-real time. Google did not presume to know which queries would prove to be the best indicators. Instead, it ran all the
terms through an algorithm that ranked how well they correlated with flu outbreaks. Then, the system tried combining the terms to see
if that improved the model. Finally, after running nearly half a billion calculations against the data, Google identified 45 terms–words
such as “headache” and “runny nose”–that had a strong correlation with the CDC’s data on flu outbreaks. All 45 terms related in
some way to influenza. But with a billion searches a day, it would have been impossible for a person to guess which ones might work
best and test only those. Moreover, the data were imperfect. Since the data were never intended to be used in this way, misspellings
and incomplete phrases were common. But the sheer size of the data set more than compensated for its messiness. The result, of
course, was simply a correlation. It said nothing about the reasons why someone performed any particular search. Was it because
the person felt ill, or heard sneezing in the next cubicle, or felt anxious after reading the news? Google’s system doesn’t know, and it
doesn’t care. Indeed, last December, it seems that Google’s system may have overestimated the number of flu cases in the United
States. This serves as a reminder that predictions are only probabilities and are not always correct, especially when the basis for the
prediction–Internet searches–is in a constant state of change and vulnerable to outside influences, such as media reports. Still, big
data can hint at the general direction of an ongoing development, and Google’s system did just that.
BACK-END OPERATIONS
Many technologists believe that big data traces its lineage back to the digital revolution of the 1980s, when advances in
microprocessors and computer memory made it possible to analyze and store ever more information. That is only superficially the
case. Computers and the Internet certainly aid big data by lowering the cost of collecting, storing, processing, and sharing
information. But at its heart, big data is only the latest step in humanity’s quest to understand and quantify the world. To appreciate
how this is the case, it helps to take a quick look behind us. Appreciating people’s posteriors is the art and science of Shigeomi
Koshimizu, a professor at the Advanced Institute of Industrial Technology in Tokyo. Few would think that the way a person sits
constitutes information, but it can. When a person is seated, the contours of the body, its posture, and its weight distribution can all be
quantified and tabulated. Koshimizu and his team of engineers convert backsides into data by measuring the pressure they exert at
360 different points with sensors placed in a car seat and by indexing each point on a scale of zero to 256. The result is a digital code
that is unique to each individual. In a trial, the system was able to distinguish among a handful of people with 98 percent accuracy.
The research is not asinine. Koshimizu’s plan is to adapt the technology as an antitheft system for cars. A vehicle equipped with it
could recognize when someone other than an approved driver sat down behind the wheel and could demand a password to allow the
car to function. Transforming sitting positions into data creates a viable service and a potentially lucrative business. And its
usefulness may go far beyond deterring auto theft. For instance, the aggregated data might reveal clues about a relationship between
drivers’ posture and road safety, such as telltale shifts in position prior to accidents. The system might also be able to sense when a
driver slumps slightly from fatigue and send an alert or automatically apply the brakes. Koshimizu took something that had never
been treated as data–or even imagined to have an informational quality–and transformed it into a numerically quantified format.
There is no good term yet for this sort of transformation, but “datafication” seems apt. Datafication is not the same as digitization,
which takes analog content–books, films, photographs–and converts it into digital information, a sequence of ones and zeros that
computers can read. Datafication is a far broader activity: taking all aspects of life and turning them into data. Google’s augmentedreality glasses datafy the gaze. Twitter datafies stray thoughts. LinkedIn datafies professional networks. Once we datafy things, we
can transform their purpose and turn the information into new forms of value. For example, IBM was granted a U.S. patent in 2012 for
“securing premises using surface-based computing technology”–a technical way of describing a touch-sensitive floor covering,
somewhat like a giant smartphone screen. Datafying the floor can open up all kinds of possibilities. The floor could be able to identify
the objects on it, so that it might know to turn on lights in a room or open doors when a person entered. Moreover, it might identify
individuals by their weight or by the way they stand and walk. It could tell if someone fell and did not get back up, an important feature
for the elderly. Retailers could track the flow of customers through their stores. Once it becomes possible to turn activities of this kind
into data that can be stored and analyzed, we can learn more about the world–things we could never know before because we could
not measure them easily and cheaply.
BIG DATA IN THE BIG APPLE
Big data will have implications far beyond medicine and consumer goods: it will profoundly change how governments work and alter
the nature of politics. When it comes to generating economic growth, providing public services, or fighting wars, those who can
harness big data effectively will enjoy a significant edge over others. So far, the most exciting work is happening at the municipal
level, where it is easier to access data and to experiment with the information. In an effort spearheaded by New York City Mayor
Michael Bloomberg (who made a fortune in the data business), the city is using big data to improve public services and lower costs.
One example is a new fire-prevention strategy. Illegally subdivided buildings are far more likely than other buildings to go up in
flames. The city gets 25,000 complaints about overcrowded buildings a year, but it has only 200 inspectors to respond. A small team
of analytics specialists in the mayor’s office reckoned that big data could help resolve this imbalance between needs and resources.
The team created a database of all 900,000 buildings in the city and augmented it with troves of data collected by 19 city agencies:
records of tax liens, anomalies in utility usage, service cuts, missed payments, ambulance visits, local crime rates, rodent complaints,
and more. Then, they compared this database to records of building fires from the past five years, ranked by severity, hoping to
uncover correlations. Not surprisingly, among the predictors of a fire were the type of building and the year it was built. Less
expected, however, was the finding that buildings obtaining permits for exterior brickwork correlated with lower risks of severe fire.
Using all this data allowed the team to create a system that could help them determine which overcrowding complaints needed urgent
attention. None of the buildings’ characteristics they recorded caused fires; rather, they correlated with an increased or decreased
risk of fire. That knowledge has proved immensely valuable: in the past, building inspectors issued vacate orders in 13 percent of
their visits; using the new method, that figure rose to 70 percent–a huge efficiency gain. Of course, insurance companies have long
used similar methods to estimate fire risks, but they mainly rely on only a handful of attributes and usually ones that intuitively
correspond with fires. By contrast, New York City’s big-data approach was able to examine many more variables, including ones that
would not at first seem to have any relation to fire risk. And the city’s model was cheaper and faster, since it made use of existing
data. Most important, the big-data predictions are probably more on target, too. Big data is also helping increase the transparency of
democratic governance. A movement has grown up around the idea of “open data,” which goes beyond the freedom-of-information
laws that are now commonplace in developed democracies. Supporters call on governments to make the vast amounts of innocuous
data that they hold easily available to the public. The United States has been at the forefront, with its Data.gov website, and many
other countries have followed. At the same time as governments promote the use of big data, they will also need to protect citizens
against unhealthy market dominance. Companies such as Google, Amazon, and Facebook–as well as lesser-known “data brokers,”
such as Acxiom and Experian–are amassing vast amounts of information on everyone and everything. Antitrust laws protect against
the monopolization of markets for goods and services such as software or media outlets, because the sizes of the markets for those
goods are relatively easy to estimate. But how should governments apply antitrust rules to big data, a market that is hard to define
and that is constantly changing form? Meanwhile, privacy will become an even bigger worry, since more data will almost certainly
lead to more compromised private information, a downside of big data that current technologies and laws seem unlikely to prevent.
Regulations governing big data might even emerge as a battleground among countries. European governments are already
scrutinizing Google over a raft of antitrust and privacy concerns, in a scenario reminiscent of the antitrust enforcement actions the
European Commission took against Microsoft beginning a decade ago. Facebook might become a target for similar actions all over
the world, because it holds so much data about individuals. Diplomats should brace for fights over whether to treat information flows
as similar to free trade: in the future, when China censors Internet searches, it might face complaints not only about unjustly muzzling
speech but also about unfairly restraining commerce.
BIG DATA OR BIG BROTHER?
States will need to help protect their citizens and their markets from new vulnerabilities caused by big data. But there is another
potential dark side: big data could become Big Brother. In all countries, but particularly in nondemocratic ones, big data exacerbates
the existing asymmetry of power between the state and the people. The asymmetry could well become so great that it leads to bigdata authoritarianism, a possibility vividly imagined in science-fiction movies such as Minority Report. That 2002 film took place in a
near-future dystopia in which the character played by Tom Cruise headed a “Precrime” police unit that relied on clairvoyants whose
visions identified people who were about to commit crimes. The plot revolves around the system’s obvious potential for error and,
worse yet, its denial of free will. Although the idea of identifying potential wrongdoers before they have committed a crime seems
fanciful, big data has allowed some authorities to take it seriously. In 2007, the Department of Homeland Security launched a
research project called FAST (Future Attribute Screening Technology), aimed at identifying potential terrorists by analyzing data
about individuals’ vital signs, body language, and other physiological patterns. Police forces in many cities, including Los Angeles,
Memphis, Richmond, and Santa Cruz, have adopted “predictive policing” software, which analyzes data on previous crimes to identify
where and when the next ones might be committed. For the moment, these systems do not identify specific individuals as suspects.
But that is the direction in which things seem to be heading. Perhaps such systems would identify which young people are most likely
to shoplift. There might be decent reasons to get so specific, especially when it comes to preventing negative social outcomes other
than crime. For example, if social workers could tell with 95 percent accuracy which teenage girls would get pregnant or which high
school boys would drop out of school, wouldn’t they be remiss if they did not step in to help? It sounds tempting. Prevention is better
than punishment, after all. But even an intervention that did not admonish and instead provided assistance could be construed as a
penalty–at the very least, one might be stigmatized in the eyes of others. In this case, the state’s actions would take the form of a
penalty before any act were committed, obliterating the sanctity of free will. Another worry is what could happen when governments
put too much trust in the power of data. In his 1999 book, Seeing Like a State, the anthropologist James Scott documented the ways
in which governments, in their zeal for quantification and data collection, sometimes end up making people’s lives miserable. They
use maps to determine how to reorganize communities without first learning anything about the people who live there. They use long
tables of data about harvests to decide to collectivize agriculture without knowing a whit about farming. They take all the imperfect,
organic ways in which people have interacted over time and bend them to their needs, sometimes just to satisfy a desire for
quantifiable order. This misplaced trust in data can come back to bite. Organizations can be beguiled by data’s false charms and
endow more meaning to the numbers than they deserve. That is one of the lessons of the Vietnam War. U.S. Secretary of Defense
Robert McNamara became obsessed with using statistics as a way to measure the war’s progress. He and his colleagues fixated on
the number of enemy fighters killed. Relied on by commanders and published daily in newspapers, the body count became the data
point that defined an era. To the war’s supporters, it was proof of progress; to critics, it was evidence of the war’s immorality. Yet the
statistics revealed very little about the complex reality of the conflict. The figures were frequently inaccurate and were of little value as
a way to measure success. Although it is important to learn from data to improve lives, common sense must be permitted to override
the spreadsheets.
HUMAN TOUCH
Big data is poised to reshape the way we live, work, and think. A worldview built on the importance of causation is being challenged
by a preponderance of correlations. The possession of knowledge, which once meant an understanding of the past, is coming to
mean an ability to predict the future. The challenges posed by big data will not be easy to resolve. Rather, they are simply the next
step in the timeless debate over how to best understand the world.
Still, big data will become integral to addressing many of the world’s pressing problems. Tackling climate change will require
analyzing pollution data to understand where best to focus efforts and find ways to mitigate problems. The sensors being placed all
over the world, including those embedded in smartphones, provide a wealth of data that will allow climatologists to more accurately
model global warming. Meanwhile, improving and lowering the cost of health care, especially for the world’s poor, will make it
necessary to automate some tasks that currently require human judgment but could be done by a computer, such as examining
biopsies for cancerous cells or detecting infections before symptoms fully emerge.
Ultimately, big data marks the moment when the “information society” finally fulfills the promise implied by its name. The data take
center stage. All those digital bits that have been gathered can now be harnessed in novel ways to serve new purposes and unlock
new forms of value. But this requires a new way of thinking and will challenge institutions and identities. In a world where data shape
decisions more and more, what purpose will remain for people, or for intuition, or for going against the facts? If everyone appeals to
the data and harnesses big-data tools, perhaps what will become the central point of differentiation is unpredictability: the human
element of instinct, risk taking, accidents, and even error. If so, then there will be a special need to carve out a place for the human:
to reserve space for intuition, common sense, and serendipity to ensure that they are not crowded out by data and machine-made
answers.
This has important implications for the notion of progress in society. Big data enables us to experiment faster and explore more leads.
These advantages should produce more innovation. But at times, the spark of invention becomes what the data do not say. That is
something that no amount of data can ever confirm or corroborate, since it has yet to exist. If Henry Ford had queried big-data
algorithms to discover what his customers wanted, they would have come back with “a faster horse,” to recast his famous line. In a
world of big data, it is the most human traits that will need to be fostered–creativity, intuition, and intellectual ambition–since human
ingenuity is the source of progress.
Big data is a resource and a tool. It is meant to inform, rather than explain; it points toward understanding, but it can still lead to
misunderstanding, depending on how well it is wielded. And however dazzling the power of big data appears, its seductive glimmer
must never blind us to its inherent imperfections. Rather, we must adopt this technology with an appreciation not just of its power but
also of its limitations.
KENNETH CUKIER is Data Editor of The Economist. VIKTOR MAYER-SCHOENBERGER is Professor of Internet Governance and
Regulation at the Oxford Internet Institute. They are the authors of Big Data: A Revolution That Will Transform How We Live, Work,
and Think (Houghton Mifflin Harcourt, 2013), from which this essay is adapted. Copyright [c] by Kenneth Cukier and Viktor MayerSchoenberger. Reprinted by permission of Houghton Mifflin Harcourt.
Copyright: COPYRIGHT 2013 Council on Foreign Relations, Inc.
http://www.foreignaffairs.org
Source Citation (MLA 9th Edition)
Cukier, Kenneth, and Viktor Mayer-Schoenberger. “The rise of big data: how it’s changing the way we think about the world.” Foreign
Affairs, vol. 92, no. 3, May-June 2013. Gale In Context: Global Issues,
link.gale.com/apps/doc/A329302643/GIC?u=csudh&sid=Primo&xid=6fb4c0bb. Accessed 5 Oct. 2021.
Gale Document Number: GALE|A329302643
The Rise of Big Data Policing
The Rise of Big Data Policing
Surveillance, Race, and the Future of Law Enforcement
Andrew Guthrie Ferguson
New York University Press
New York
NEW YORK UNIVERSITY PRESS
New York
www.nyupress.org
© 2017 by New York University
All rights reserved
References to Internet websites (URLs) were accurate at the time of writing. Neither the author
nor New York University Press is responsible for URLs that may have expired or changed since
the manuscript was prepared.
Library of Congress Cataloging-in-Publication Data
Names: Ferguson, Andrew G., author.
Title: The rise of big data policing : surveillance, race, and the future of law enforcement /
Andrew Guthrie Ferguson.
Description: New York : New York University Press, [2017] | Includes bibliographical
references and index.
Identifiers: LCCN 2017012924| ISBN 9781479892822 (cl ; alk. paper) | ISBN 1479892823 (cl ;
alk. paper)
Subjects: LCSH: Law enforcement—United States—Data processing. | Police—United States—
Data processing. | Big data—United States. | Data mining in law enforcement—United States. |
Electronic surveillance—United States. | Criminal statistics—United States. | Discrimination in
law enforcement—United States. | Racial profiling in law enforcement—United States.
Classification: LCC HV8141 .F47 2017 | DDC 363.2/32028557—dc23
LC record available at https://lccn.loc.gov/2017012924
New York University Press books are printed on acid-free paper, and their binding materials are
chosen for strength and durability. We strive to use environmentally responsible suppliers and
materials to the greatest extent possible in publishing our books.
Manufactured in the United States of America
10 9 8 7 6 5 4 3 2 1
Also available as an ebook
To the police, citizens, and advocates interested in improving policing, thank
you for engaging these ideas.
To my colleagues at the UDC David A. Clarke School of Law, thank you for
encouraging my scholarship.
To Alissa, Cole, Alexa, Mom and Dad, thank you for everything.
Contents
Introduction: Big Data Policing
1. Big Data’s Watchful Eye: The Rise of Data Surveillance
2. Data Is the New Black: The Lure of Data-Driven Policing
3. Whom We Police: Person-Based Predictive Targeting
4. Where We Police: Place-Based Predictive Policing
5. When We Police: Real-Time Surveillance and Investigation
6. How We Police: Data Mining Digital Haystacks
7. Black Data: Distortions of Race, Transparency, and Law
8. Blue Data: Policing Data
9. Bright Data: Risk and Remedy
10. No Data: Filling Data Holes
Conclusion: Questions for the Future
Notes
Index
About the Author
Introduction
Big Data Policing
A towering wall of computer screens blinks alive with crisis. A digital map of
Los Angeles alerts to 911 calls. Television screens track breaking news
stories. 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 center in downtown Los Angeles forecasts the
future of policing in America.
Welcome to the Los Angeles 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 big 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 name and a physical description into the
computer—two fragmented clues that would 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 pop up in sortable fields. By matching known attributes, the
computer narrows the search 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 follow up with a
witness to identify the car used in the robbery. A match leads to an arrest and
a closed case.
A 911 call. A possible gang fight in progress. RACR Command directs
patrol units to the scene all the while monitoring their real-time progress.
Data about the fight is pushed to officers on their mobile phones. Alerts
about past shootings and gang tensions warn officers of unseen dangers.
Neighborhood histories get mapped for insight. Officers scroll through
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photographs to visualize the physical geography before they arrive. All of the
data is instantaneously sent to officers, allowing them to see the risks before
they need to act.
Roll call. Monday morning. Patrol officers receive digital maps of today’s
“crime forecast.” Small red 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
additional attention to these “hot” areas during the shift. Every day, police
wait in the predicted locations looking for the forecast crime. The theory: put
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 find suspicious activity from billions of otherwiseanonymous consumer transactions and personal communications. By
digitizing faces, communications, and patterns, police will instantly and
accurately be able to investigate billions of all-too-human clues.
This is the future. This is the present. This is the beginning of big data
policing.
Big data technologies and predictive analytics will revolutionize policing.
Predictive policing, intelligence-driven prosecution, “heat lists” of targets,
social media scraping, data mining, and a data-driven surveillance state
provide the first clues to how the future of law enforcement will evolve.
At the center 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 big data arises from the
expanded ability 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 watch 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 collect health, educational, and
criminal records. Detectives monitor public Facebook, YouTube, and
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Twitter feeds. Aggregating data centers sort and study the accumulated
information in local and federally funded fusion centers. This is the big data
world of law enforcement—still largely in its infancy but offering vastly
more incriminating bits of data to use and study.
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 street corner most likely to see
the next 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 now starts with
powerful computers crunching large data sets almost 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 engage in crime, some not. Some live in poverty,
some not. But all now find themselves encircled by big data’s reach. The
math behind big 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 real human experience, and many
times those experiences remain fraught with all-too-human bias, fear, distrust,
and racial tension. For those communities, especially poor communities of
color, these data-collection efforts cast a dark shadow on the future.
This book shines light on the “black data” arising from big data policing:
“black” as in opaque, because the data exists largely hidden within complex
algorithms; “black” as in racially coded, because the data directly impacts
communities of color; “black” as in the next new thing, given legitimacy and
prominence due to the perception that data-driven anything is cool, technofriendly, and futuristic; and, finally, “black” as distorting, creating legal
shadows and constitutional gaps where the law used to see clearly. Black data
matters because it has real-world impacts. Black data marks human “threats”
with permanent digital suspicion and targets poor communities of color.
Black data leads to aggressive use of police force, including deadly force, and
new forms of invasive surveillance. Big data policing, and these new forms
of surveillance and social control, must confront this black data problem.
This book examines how big data policing impacts the “who,” “where,”
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“when,” and “how” of policing. New technologies threaten to impact all
aspects of policing, and studying the resulting distortions provides a
framework to evaluate all future surveillance technologies. A race is on to
transform policing. New developments in consumer data collection have
merged with law enforcement’s desire to embrace “smart policing” principles
in an effort to increase 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.
This book arises out of the intersection of two cultural shifts in policing.
First, predictive analytics, social network theory, and data-mining technology
have all developed to a point of sophistication such that big data policing is
no longer a futuristic idea. Although police have long collected information
about suspects, now this data can be stored in usable and sharable databases,
allowing for greater surveillance potential. Whereas in an earlier era a police
officer might see a suspicious man on the street and have no context about his
past or future danger, soon digitized facial-recognition technologies will
identify him, crime data will detail his criminal history, algorithms will rate
his risk level, and a host of citywide surveillance images will provide context
in the form of video surveillance for his actions over the past few hours. Big
data will illuminate the darkness of suspicion. But it also will expand the lens
of who can be watched.
The second cultural shift in policing involves the need to respond to
outrage arising from police killings of unarmed African Americans in
Ferguson, Missouri; Staten Island, New York; Baltimore, Maryland;
Cleveland, Ohio; Charleston, South Carolina; Baton Rouge, Louisiana;
Falcon Heights, Minnesota; and other cities. This sustained national protest
against police—and the birth of the Movement for Black Lives—brought to
the surface decades of frustration about racially discriminatory law
enforcement practices. Cities exploded in rage over unaccountable police
actions. In response, data-driven policing began to be sold as one answer to
racially discriminatory policing, offering a seemingly race-neutral,
“objective” justification for police targeting of poor communities. Despite
the charge that police data remains tainted by systemic bias, police
administrators can justify continued aggressive police practices using datadriven metrics. Predictive policing systems offer a way seemingly to turn the
page on past abuses, while still legitimizing existing practices.
For that reason, my aim in this book is to look at the dangers of black data
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arising at this moment in history. Only by understanding why the current big
data policing systems were created and how traditional policing practices fit
within those systems can society evaluate the promise of this new approach
to data-driven law enforcement. Black data must be illuminated to see how it
might be abused. 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 must confront
those concerns before—not after—the technology’s implementation. And
society must confront those challenges informed by an understanding of how
race has fractured and delegitimized the criminal justice system for many
citizens. Black data, of course, is not just about African Americans, although
the history of racially discriminatory policing runs deep in certain
communities. But black data exposes how all marginalized communities face
a growing threat from big data policing systems. 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 trail 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.
Big data is not all dystopian. The insights of big data policing need not be
limited to targeting criminal activity. The power of predictive analytics can
also be used to identify police misconduct or identify the underlying social
and economic needs that lead to crime. In an era of heighted concern with
police accountability, new surveillance technologies offer new avenues to
watch, monitor, and even predict police misconduct. Systems of “blue data”
can be created to help “police the police.” Similarly, big data technologies
can be redirected to identify and target social, economic, or environmental
risk factors. This is the promise of “bright data,” in which the surveillance
architecture developed to police criminal risk can be redirected to address
environmental risks and social needs. After all, just because big data policing
identifies the risk, this does not mean that law enforcement must provide the
remedy.
The big 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 big 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 big
data analytics. New technologies will open up new opportunities for
investigation and surveillance. The technological environment is rich with
possibility but also danger. This book seeks to initiate 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 plan for its big data
future.
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Big Data’s Watchful Eye
The Rise of Data Surveillance
The world is full of obvious things which nobody by any chance ever observes.
—Sherlock Holmes1
Data Trails
You are being watched. Surveilled. Tracked. Targeted. Every search on the
internet recorded. Every purchase at the store documented. Every place you
travel mapped. They know how fast you drive, your preferred cereal, your
dress size. They know your financial situation, all of your past jobs, your
credit limit. They know your health concerns, reading preferences, and
political voting patterns. They also know your secrets. They have been
watching for years. In truth, you live in a surveillance state. The watchers
know you because of the data you leave behind.
But it is not just you. These watchers also know about your family, friends,
neighbors, colleagues, clubs, and associates. They see the circles you contact,
the friends you ignore, and the political issues you embrace. They see you as
part of a group, but they also see all the other parts of the group. Links
expand outward, so that all of your contacts can be visualized as a web of
interrelated, interconnected groups.
Welcome to the world of big data, where one’s data trail reveals the mosaic
of lived experience and has become the currency of a new economy. “They”
are companies, companies that enable a digital world by offering
convenience, information, and services all in return for one thing: data. Your
personal data and interests—all of those points of commercial interaction,
consumer choice, “likes,” links, and loves—have been vacuumed up,
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processed, and sold to others wanting to get to know you. Currently, this
widespread surveillance remains in the hands of for-profit companies, for the
purpose of offering consumers convenience and choice. But law enforcement
is interested too. And most of this information is a subpoena (or warrant)
away from being part of a criminal case. The investigative lure of big data
technologies is just too powerful to ignore.
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What Is Big Data?
To understand the potential of big data policing, the scope of big data must be
explored. So what is big data? In general, “big data” is a shorthand term for
the collection and analysis of large data sets with the goal to reveal hidden
patterns or insights. A report from the Executive Office of the President
summarized: “There are many definitions of ‘big data’ which may differ
depending on whether you are a computer scientist, a financial analyst, or an
entrepreneur pitching an idea to a venture capitalist. Most definitions reflect
the growing technological ability to capture, aggregate, and process an evergreater volume, velocity, and variety of data.” In simple terms, large
collections of data can be sorted by powerful computers to visualize
unexpected connections or correlations. Machine-learning tools and
predictive analytics allow educated guesses about what the correlations
mean.
A simple example of how big data works can be seen at Amazon.com.
Beneath each item for sale is a recommendation section that displays
information about what “customers who bought this item also bought” and
items that are “frequently bought together.” Amazon generates these
suggestions from the purchasing patterns of its 300 million customers who
bought related items. Correlating the historical data of billions of transactions
leads to an insight into which goods customers usually purchase together.
Amazon, of course, also knows everything you have ever bought from the
company. But Amazon can sort the purchasing data of any particular product
to show the consumer patterns of all past customers. Amazon can use that
large data set to predict what items you might actually want in the future.
After all, if you bought a coffee maker today, you may need coffee
tomorrow.
A more unusual example involves the correlation between Pop-Tarts and
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hurricanes. Walmart—a company that collects more than two and half
petabytes of data every hour from customers (equivalent to 50 million fourdrawer filing cabinets filled with text)—discovered that just before a
hurricane, people buy an unusual amount of Strawberry Pop-Tarts. Why?
No one really knows. Perhaps the reason for the uptick is because Pop-Tarts
are nonperishable comfort food, and sometimes sugary comfort is just what
you need after a big storm. Or perhaps not. Big data demonstrates the
correlation, not the cause. It offers insight without explanation—a reality that
is both useful and unsettling.
Obviously, big companies like Amazon and Walmart collect personal data,
but what is the extent of big data collection across our daily lives? More than
can be comprehended. As Julia Angwin termed it, “We are living in a
Dragnet Nation—a world of indiscriminate tracking where institutions are
stockpiling data about individuals at an unprecedented pace.” The World
Privacy Forum—a watchdog group on personal privacy—estimates that there
are 4,000 different databases collecting information on us. Every time we
interact with computers, sensors, smartphones, credit cards, electronics, and
much, much more, we leave a digital trail that is revealing of ourselves and
valuable to others. These are the breadcrumbs of the big data maze. Follow
them and they lead right back to you.
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Where Does Big Data Come From?
Big data comes from you. You provide the building blocks of big data’s
power in small digital bits.
Think of the normal patterns of your life. You probably live in a house or
an apartment. Even if you do not live in a wired “smart home” that comes
equipped with a “smart fridge” to order milk when you run out, or a Nest
“smart thermostat” to turn down the heat when you leave, your home does
reveal basic data about your lifestyle. You have an address. The address
reveals general information about your income (as implied by the cost of the
home) and your family size (number of bedrooms). Your zip code provides
clues about demographics, wealth, and political sentiment.
You probably get mail at that address. First to note, the United States
Postal System runs the Mail Isolation Control and Tracking program, which
photographs the exterior of every single piece of mail processed in the United
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States. So data about your address is tracked along with the 150 billion
letters mailed each year. But more obviously, your mail also reveals things
about you. Magazine subscriptions reveal your political and cultural interests,
and catalogues reveal your hobbies and shopping preferences. Mail reveals
your friends and associates, just as packages reveal your styles, interests, and
lifestyle choices. Even junk mail says something about what marketers think
you want.
You likely also use the internet. Some of those packages came from online
shopping. Those online retail companies track your purchases and even those
things you looked at but did not purchase. Inferences from those purchases
are also valuable. If you bought infant diapers for the first time, you might
also need age-appropriate children’s toys for the next holiday season (and for
the next 18 years). If you bought a “how to quit smoking book,” you might
not be the best candidate for a new cigar magazine. But you don’t even have
to shop to give up your data. Google records every internet search, really
every click of the mouse. That means every health query, travel question,
childrearing tip, news article, and entertainment site. Google and other search
engines provide little windows into your thinking (if not your soul). Your
internet protocol (IP) provides your exact location, and while your IP
addresses might change as you switch from your home computer to your
iPhone to your work computer, Hotmail knows where you are at all times.
Amazon knows the page you stopped reading on your Kindle ebook reader.
Your cable provider (which may also be your cellphone and wireless
provider) knows what TV shows you watch late at night. Netflix and other
streaming entertainment services rely on personalized predictive formulas
based on past viewing data.
Social media expands the web of data from yourself to your friends and
associates. On Facebook, you literally display your “likes” of certain things.
Professional sites like LinkedIn add more information about what you do,
who you know, and what accolades you have received. Personal and social
updates broadcast life changes, and charity or community service activities
get promoted. Photos provide data about where you have been and who you
were with. Geotagging of information from those photos and other services
reveal the time, location, and date of the picture. Facial recognition links
people together, so that your photos (and thus your identity) can be tracked
over different social media platforms. And sometimes you might simply tell
people on Twitter what you are doing or upload photos of your dinner entrée
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on Instagram or Snapchat.
You might leave your home in a car—a car registered to your address with
a name, gender, birthdate, and identification number. The car can be tracked
through a city via surveillance cameras, electronic toll collectors, or
automated license-plate scanners. Your type of car (hybrid or Hummer)
might reveal a lifestyle preference or environmental worldview. The car itself
might have Global Positioning System (GPS) tracking through something
like a GM OnStar program to allow for instant help in an accident or
emergency. But that helpful service requires constant locational tracking. Or
maybe you have an insurance provider that monitors real-time driving data of
your habits in return for lower car-insurance rates. You drive carefully, you
save money.
But, no matter, if you possess a smartphone with locational services turned
on, the speed, location, and direction of your car is being monitored in real
time. Your iPhone knows a wealth of locational information about where
you go, which health clinic you stopped at, and the Alcoholics Anonymous
meeting you just attended. Locational data from Google Maps tracks your
church attendance, political protests, and friends. Other mobile apps leech
data to companies in return for targeted advertisements or travel tips.
Games, services, geotracking ads, emergency calls—all depend on location.
Everything that little pocket computer does can be tracked and recorded in
granular detail. That means that every YouTube video, every photograph, and
every check of the weather is collected, to reveal the things you do on a daily
basis, as well as where you were when you did them.
Maybe you took that car to work. Your employment history has been
harvested by credit agencies. Your job, finances, professional history, and
even your education are recorded. Maybe you went shopping. That
customer-loyalty card offering in-store discounts also tracks each purchase
you make. Stores know not only everything you have purchased going back
years but also your physical location when you made the purchase. Maybe
you went to the bank. All of your financial information, account balances,
late fees, investments, credit history—all are recorded. Your credit card
statement is a little reminder of everything you did and where you did it for
the past month. Maybe you took the car to have fun. The Google search of
local restaurant reviews followed by a map search of a particular restaurant
and an Open Table reservation provide a pretty good prediction of your
Saturday-night plans.
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If you add in “smart devices” connected through the Internet of Things
(Fitbits, smart bandages, smart cups) or sensors built into our transportation
infrastructure, clothing, and bodies, you have a very revealing web of data
about our activities. Researchers predict that there will be over 50 billion
smart things connected among the “Internet of Everything” by 2020. These
“smart devices” are scarily aware of you. If your television responds to your
voice or your electronic personal assistant answers your questions, it means
these smart devices are always listening and always on.
Finally, public records filled with census data, property records, licenses,
Department of Motor Vehicle information, bankruptcies, criminal
convictions, and civil judgments can be purchased by companies seeking to
understand us. This official, bureaucratic record of life, linked as it is to
governmental data systems, has become the foundation for many credit
histories and personalized data dossiers on individuals.
This is how big data becomes big. This is why big data can be such a threat
to privacy, associational freedom, and autonomy. Your self-surveillance
provides the currency for commercial profit but also the building blocks for
an intrusive police state. Every digital clue—with the appropriate legal
process—can be demanded by police and prosecutors. Whereas in an earlier
era, only your family might know what you did, what you ate, how you
dressed, or what you thought about, now the digital clues of life online can be
collected, reassembled, and mapped to mirror this same knowledge. In fact,
your digital clues may reveal secrets you have kept hidden from your spouse,
family, or best friends.
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Who Owns the Data?
Private data brokers collect, buy, and sell personal data to companies
interested in selling products, determining financial credit risk, or conducting
employment background investigations. Data brokers sell your data to others
—including law enforcement—for investigative purposes.
Data brokers challenge conventional assumptions about individual privacy.
Aggregated private transactions are repurposed and repackaged into a
composite targeted profile of you as a consumer. The United States Senate
Commerce Committee detailed how big data companies like Acxiom claim to
have information on over 700 million consumers worldwide with over 3,000
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data segments for nearly every U.S. consumer. Another company,
Datalogix, claims to have data on almost every U.S. household. Much of this
information is demographic, such as name, address, telephone number, email,
gender, age, marital status, children, educational level, and political
affiliation. Some of the information is available through consumer
transactions, detailing where one bought something, and some of the
information focuses on health problems and medical data. The Senate report
detailed how “one company collects data on whether consumers suffer from
particular ailments, including Attention Deficit Hyperactivity Disorder,
anxiety, depression, diabetes, high blood pressure, insomnia, and
osteoporosis, among others; another keeps data on the weights of individuals
in a household.” And “an additional company offers for sale lists of
consumers under 44 different categories of health conditions, including
obesity, Parkinson’s disease, Multiple Sclerosis, Alzheimer’s disease, and
cancer, among others.”
The level of detail can be remarkably creepy. Here are two excerpts from
the Senate Commerce Committee’s report:
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Equifax maintains approximately 75,000 individual data
elements for its use in creating marketing products, including
information as specific as whether a consumer purchased a
particular soft drink or shampoo product in the last six months,
uses laxatives or yeast infection products, OB/GYN doctor
visits within the last 12 months, miles traveled in the last 4
weeks, and the number of whiskey drinks consumed in the
past 30 days.
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Some companies offer “data dictionaries” that include more
than one thousand potential data elements, including whether
the individual or household is a pet owner, smokes, has a
propensity to purchase prescriptions through the mail, donates
to charitable causes, is active military or a veteran, holds
certain insurance products including burial insurance or
juvenile life insurance, enjoys reading romance novels, or is a
hunter.
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The companies know if you have allergies, if you smoke or wear contacts, if
your elderly parents live with you, if you speak Spanish, the type of roof on
your house, and if you have more than 250 Twitter followers. The
creepiness crosses into almost comedic stereotypes as large groups of people
become lumped together on the basis of shared demographics or income.
Data brokers segment out certain groups. Single men and women over age 66
with “low educational attainment and low net worths” are targeted as “Rural
Everlasting.” Other singles in the same age group but with more disposable
income are known as “Thrifty Elders.” Certain low-income minority groups
composed of African Americans and Latinos are labeled as “Urban
Scramble” or “Mobile Mixers.” Private data companies regularly sell and
repackage this information about consumer activity to other data brokers,
further expanding the webs of shared data.
If you think about what big data companies do in the consumer space, you
will see the allure for law enforcement. Data brokers collect personal
information to monitor individuals’ interests and inclinations. They
investigate connections among groups of like-minded people and uncover
patterns in the data to reveal hidden insights. This is also what law
enforcement investigators do with criminal suspects and gangs. Police
monitor, investigate, uncover, and target. Police look for suspicious patterns.
Police watch. The tools of big data are the tools of surveillance, and law
enforcement relies on surveillance to solve and prevent crime.
Unsurprisingly, police have shown great interest in the possibilities of big
data policing.
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A Permanent Digital Record
The first step in solving any crime is analyzing the clues. Knowing who
might be the likely suspect has been part of policing since the mid-1700s,
when courts first recorded those who were thought to have been involved in a
fraud or felony. Unsurprisingly, as policing developed in sophistication, so
did data collection and use. The modern “police blotter” now sits on a cloud
server accessible to officers across the jurisdiction or the country.
Federal databases like the National Crime Information Center (NCIC)
contain 13 million active records, all searchable by police officers on the
street or in their patrol cars. In a routine traffic stop, if a police officer “runs
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your name” through the system, NCIC will provide personal details about
any arrests, warrants, gang affiliations, terrorism ties, supervised release, or
fugitive status, as well as information about property including gun
ownership, car and boat licenses, and even if you have been the victim of
identity theft. This massive database filled with state, local, and federal
information is reportedly accessed 12 million times a day by authorities. The
federal government also maintains watch lists focused on terrorism, including
700,000 names in the Terrorist Screening Database (TSD), a million names in
the Terrorist Identities Datamart Environment (TIDE), and 50,000 names on
the “No-Fly List.”
States also collect and generate data sets to monitor citizens. Eleven states
maintain extensive electronic gang databases on suspected gang members.
Over 800,000 men and women are listed in federal and state sex-offender
registries for convicted sex offenders. Individuals convicted of gun crimes in
some states have been required to register. Details about where these
offenders live, work, and go to school; what cars they drive; and even their
appearance (tattoos, facial hair, scars) are updated constantly in digital
archives.
After the terrorist attacks of September 11, 2001, federal and state officials
joined forces to establish a national intelligence strategy to improve criminal
justice data collection and information sharing. A vast array of law
enforcement organizations now share personal data about suspects, crimes,
and crime patterns. These organizations include state, local, tribal, and
territorial agencies, the Department of Justice (DOJ), the Department of
Homeland Security (DHS), the Federal Bureau of Investigation (FBI), the
Drug Enforcement Administration (DEA), and the Bureau of Alcohol,
Tobacco, and Firearms (ATF). A network of fusion centers seeks to share
threat-related information across federal and state lines. Regional
Information Sharing Systems (RISS) Centers coordinate incoming data, while
Crime Analysis Centers (CACs) analyze collected data. These new datasharing entities also coordinate with the 17 different agencies that make up
the United States intelligence community, including outward, internationalfacing data-collection agencies like the National Security Agency (NSA) and
the Central Intelligence Agency (CIA).
Data projects like the National Data Exchange Program (N-DEx) have
been set up “as a giant data warehouse” to pull together otherwiseincompatible police databases. As described in N-DEx’s “Privacy Impact
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Assessment,”
N-DEx provides a national investigative information sharing
system available through a secure Internet site that allows
criminal justice agencies to search and analyze data
representing the entire criminal justice cycle, including crime
incident and investigation records; arrest, booking, and
incarceration records; and probation and parole records. As a
repository of information from local, state, regional, tribal, and
federal criminal justice entities, N-DEx provides these
agencies with the capability to make linkages between crime
incidents, criminal investigations, and related events to help
solve, deter, and prevent crimes. . . . N-DEx contains the
personally identifiable information (PII) of suspects,
perpetrators, witnesses and victims, and anyone else who may
be identified in a law enforcement report concerning a crime
incident or criminal investigation.
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As of 2014, N-DEx had over 107,000 users and over 170 million searchable
records. Start-up companies have been building similar private datamanagement systems to assist law enforcement in organizing the evergrowing stores of data.
Beyond investigative records, law enforcement now collects biological
data. Biometric collection regularly includes DNA, fingerprints, photographs,
and iris and retina scans—all secured in searchable databases to investigate
crimes. The Combined DNA Index System (CODIS) includes 12 million
searchable DNA profiles. The FBI’s Next Generation Identification (NGI)
system integrates fingerprints, palm prints, facial recognition, and iris scans
in one larger searchable database. The FBI has over 23 million searchable
photographs and the largest collection of fingerprints in the world. All of
this data pushes police investigation into the future, and all of it opens the
opportunity for new big data tools to sort, search, and discover otherwise
hidden connections between crime and criminals.
Data has also revolutionized how certain police run their day-to-day
operations. Many large police departments follow the crime numbers to guide
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strategy. Some bigger police departments like the New York Police
Department (NYPD) have gone so far as to hire a director of analytics to
assist in crunching the numbers. Other police departments have partnered
with private data-analytics companies or consultants to sort and study the
collected data. Professional crime analysts routinely participate in strategy
sessions in big police departments. While relying on data differently, most
have accepted the underlying principle that the big data technologies created
for the private sector can assist police administrators working to improve
public safety. In fact, in 2009, Los Angeles Police Department (LAPD) chief
Charlie Beck wrote a seminal article, titled “Predictive Policing: What Can
We Learn from Wal-Mart and Amazon about Fighting Crime in a
Recession?,” which explicitly advocated adopting data-driven business
principles to improve policing. “Analytics,” “risk-based deployment,”
“prediction,” “data mining,” and “cost-effectiveness” all emerged as new
values and goals for the modern police professional.
Currently, consumer big data technologies and law enforcement data
systems operate on separate tracks. What Google knows is not what the FBI
knows. The NCIC system is not available to private data brokers. A
patchwork of federal privacy laws theoretically restricts the direct
governmental collection of personal identifiable information. These statutes
include the Privacy Act of 1974, Electronic Communications Privacy Act of
1986 (ECPA), and Stored Communications Act (SCA), Foreign
Intelligence Surveillance Act (FISA), E-Government Act of 2002,
Financial Privacy Act, Communications Act, Gramm-Leach-Bliley Act,
Bank Secrecy Act, Right To Financial Privacy Act, Fair Credit Reporting
Act, Health Insurance Portability and Accountability Act of 1996 (HIPAA),
Genetic Information Non-discrimination Act (GINA), Children’s Online
Privacy Protection Act (COPPA), Family Educational Rights and Privacy
Act, Telephone Records and Privacy Protection Act of 2006, and Video
Protection Privacy Act. In addition to being dated (since some were drafted
decades before the big data era), these laws do not prevent law enforcement
access. As Erin Murphy has written, “The United States Code currently
contains over twenty separate statutes that restrict both the acquisition and
release of covered information. . . . Yet across this remarkable diversity, there
is one feature that all these statutes share in common: each contains a
provision exempting law enforcement from its general terms.” Police can
obtain certain data with a court order or subpoena. With a valid warrant,
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police can obtain most anything big data companies have collected for
consumer purposes. This patchwork of privacy law also does not stop law
enforcement from purchasing the same big data information like any other
customer. Just like a private data broker, police can purchase your cellphone
and internet information directly from the companies.
A complete big data convergence between private consumer data
collection and public law enforcement collection has not yet occurred. But
the lines are blurry and growing fainter. Once data has been collected in one
place, it becomes harder and harder not to aggregate the information. Private
data becomes part of public records, and then public records become the
building blocks of private and government databases. Data gets sold and
repackaged such that the original collection point becomes obscured. If
police want to know about a suspect, and the data has been collected by
private third parties, those private companies are hard-pressed to push back
and protect the information from lawful government requests. A few
powerful technology companies have on occasion rejected government
requests for assistance in obtaining customer information or have designed
encrypted systems to avoid being in a position to provide information to
police investigators. But other companies have been good corporate citizens
and provided the information as requested.
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Big Data Tools
What big data knows is one thing, but the technology used to manipulate and
organize that data is the bigger thing.
The real promise of big data remains with the ability to sort, study, and
target within large data sets. Big data becomes intelligible because of
algorithms and the large-scale computer-processing power now available.
Algorithms are just mathematical processes established to solve a particular
task. Using algorithms, pattern-matching tools can flag abnormal financial
patterns; social network technologies can link groups via emails, addresses,
or any common variable; and predictive analytics can take data-driven
insights and forecast future events. Machine-learning algorithms powered by
artificial intelligence models can sort vast streams of data in ways
unimaginable in earlier eras. Collectively, these math tools allow data
analysts to divine insight from an otherwise overwhelming amount of
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information.
As an example of one such insight, the retail giant Target figured out a way
to predict when women are pregnant. By studying women who signed up
for an in-store baby registry, Target noticed that these self-identified pregnant
women shared a similar, repeating purchasing pattern. Pregnant women
would purchase folic acid and vitamin supplements in the first trimester (to
improve prenatal health), unscented lotion in the second trimester (due to
heightened olfactory sensitivity), and hand sanitizer close to their due dates
(to protect the newborn from germs). So now if any woman’s purchases
follow that pattern (even if she has not signed up for a baby registry), Target
flags her as pregnant. The correlation of three unrelated consumer purchases
leads to a very personal future prediction.
Big data policing is no different. Law enforcement can identify drug
dealers from patterns of supplies (purchasing tiny ziplock bags, rubber bands,
digital scales), suspicious transactions (depositing cash, high-end all-cash
purchases), and travel patterns (to and from a source city for drugs). The
information does not need to be 100% accurate (just as sometimes you
receive the wrong catalogue in the mail), but—the theory goes—better
information allows police to prioritize and target the higher risks to a
community. As Cathy O’Neil wrote in her book Weapons of Math
Destruction, just as Amazon uses data to identify the “recidivist” shopper,
police can use data to predict the future criminal.
Big data tools create the potential for big data policing. The combination of
new data sources, better algorithms, expanding systems of shared networks,
and the possibility of proactively finding hidden insights and clues about
crime has led to a new age of potential surveillance. Instead of consumer
surveillance, the goal of big data policing is criminal surveillance.
Chapter 2 looks at why police administrators have been open to big data’s
embrace. Technology has not been the only force pushing innovation. Budget
cuts after a national financial recession forced police to change. In addition,
long-standing fissures in police/community relations widened as complaints
of racial bias and unconstitutional policing grew louder. Protests challenged
continued police violence. Communities demanded change from systemic
practices of social control like aggressive stop-and-frisks. Out of this
frustration, the seemingly objective metrics of data-driven policing became
quite appealing. Turning the page on human bias or racial discrimination
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became an important spur in the adoption of big data policing. The next
chapter explores the lure of these new technologies to solve age-old policing
problems.
2
Data Is the New Black
The Lure of Data-Driven Policing
Predictive policing used to be the future. Now it is the present.
—Former New York Police Department commissioner William Bratton1
Policing Darkness
The night patrol. At night, you stay in the squad car. At night, you wait for
the radio run. At night, good guys look like bad guys. In the dark, you wait.
For police chiefs, every day is a night patrol. For years, all police leaders
could do was react, respond to the crime scene, and answer to the media. In
the dark of criminal activity, police administrators could only wait. But what
if police could see in the dark? Map the crime patterns? Illuminate the bad
guys for who they are? Such is the lure of new big data technologies. Digital
crime maps could turn pushpin maps into real-time alerts. Hot-spot policing
could predictively target specific blocks of trouble. Databases could
catalogue and monitor the bad seeds that needed to be weeded out.
Another type of darkness. Budget cuts. Layoffs. The 2007 financial
recession in the United States gutted police forces, adding to police
administrators’ woes. State taxes dried up. County and municipal budgets
shrunk. Hiring froze. Specialized training stopped. Overtime ended, and
services were reduced. From 2007 to 2013, local police agencies across the
country had to do more with less—sometimes dramatically less. Then the
federal government began cutting. Sequestration meant cuts to federal grants
for gang task forces, drug task forces, community policing projects, crimescene investigation, and juvenile diversion, among many other initiatives.
And at the same time, a fear grew that with more people out of work, crime
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rates would rise.
But what if there was an answer? What if you could equip police officers
with better information? What if smart policing technology could allow you
to do more with less? What if data provided an answer to mayors and
communities demanding a comforting response to this fear of crime? The
thinking of many police chiefs could be summed up with one question: “You
mean there is a black-box computer that can predict crime?” And with one
answer: “Sign me up.”
A Boiling Pot
“Hands Up, Don’t Shoot.” A chant. A crowd. Open hands toward the sky. A
man stares down a tactical police vehicle rolling through suburban Ferguson,
Missouri. The image of armed police officers pointing assault rifles at
protesters lit a fire in Ferguson that ended up burning downtown Baltimore
and sparking a nationwide protest movement to reform policing.
In 2014, a grand jury’s failure to indict officer Darren Wilson for the
shooting death of Michael Brown touched off a wave of protests and
attention to the problem of police violence. Following Ferguson, news story
after news story covered instances of police officers killing unarmed African
Americans. Eric Gardner in Staten Island, Tamir Rice in Cleveland, Walter
Scott in Charleston, Freddie Gray in Baltimore, Alton Sterling in Baton
Rouge, and Philando Castile in Falcon Hills, Minnesota. These stories, and
dozens more killings of citizens by police, changed the conversation of
policing in America.
Race became a central point of debate. The Black Lives Matter movement
rose up and rallied social media and communities of color to protest patterns
of racism and brutality. Streets filled with demonstrators and conflicts with
police turned violent. Cable news broadcast the events live, and this pot of
simmering anger seemed to boil over regularly.
The truth, of course, is that the pot had always been boiling.
Discriminatory police practices dating back to before slavery fueled racial
tension. Aggressive policing systems created resentment, distrust, and fear
in many minority communities. As James Comey, the director of the Federal
Bureau of Investigation (FBI), admitted in a speech after the Ferguson
protests, “All of us in law enforcement must be honest enough to
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acknowledge that much of our history is not pretty. At many points in
American history, law enforcement enforced the status quo, a status quo that
was often brutally unfair to disfavored groups. . . . It was unfair to too many
people.” In this speech on “race and hard truths,” one of the nation’s most
senior law enforcement officials recognized that police needed to move
beyond this unjust past, because certain discriminatory and constitutionally
unsound police practices had undermined trust in police. A similar sentiment
was echoed by police chief and then president of the International
Association of Chiefs of Police Terrence Cunningham. Cunningham
apologized for the role of police in contributing to citizens’ mistrust:
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There have been times when law enforcement officers,
because of the laws enacted by federal, state, and local
governments, have been the face of oppression for far too
many of our fellow citizens. In the past, the laws adopted by
our society have required police officers to perform many
unpalatable tasks, such as ensuring legalized discrimination or
even denying the basic rights of citizenship to many of our
fellow Americans. While this is no longer the case, this dark
side of our shared history has created a multigenerational—
almost inherited—mistrust between many communities of
color and their law enforcement agencies.
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Ferguson, Missouri, exemplified the past and present problem of systemic
racial discrimination. In Ferguson, the Civil Rights Division of the U.S.
Department of Justice (DOJ) documented a systemic pattern of racially
discriminatory policing primarily focused on generating revenue for the local
municipal government. These practices fueled the figurative and literal fires
of protest. Ferguson police routinely stopped African Americans more than
they did whites and for all the wrong reasons. As the DOJ Ferguson report
summarized,
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Data collected by the Ferguson Police Department from 2012
to 2014 shows that African Americans account for 85% of
vehicle stops, 90% of citations, and 93% of arrests made by
FPD [Ferguson Police Department] officers, despite
comprising only 67% of Ferguson’s population. African
Americans are more than twice as likely as white drivers to be
searched during vehicle stops even after controlling for nonrace based variables such as the reason the vehicle stop was
initiated, but are found in possession of contraband 26% less
often than white drivers, suggesting officers are impermissibly
considering race as a factor when determining whether to
search.
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Nearly 90% of the use of force incidents targeted African Americans.
Almost 95% of “failure to comply” and 94% of “walking in the roadway”
charges (misdemeanors usually associated with social-control measures)
targeted African Americans. Statistics demonstrating racial bias, personal
stories showing racial animus, and smoking-gun racist emails all painted a
dark picture that race had discolored local policing practices.
Worse, many of these unpleasant and unconstitutional police contacts were
undertaken to collect money for the municipality, not for crime control. The
most damning finding of the DOJ report revealed a financial perversion at the
heart of the policing structure in Ferguson:
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The City’s emphasis on revenue generation has a profound
effect on FPD’s approach to law enforcement. Patrol
assignments and schedules are geared toward aggressive
enforcement of Ferguson’s municipal code, with insufficient
thought given to whether enforcement strategies promote
public safety or unnecessarily undermine community trust and
cooperation. Officer evaluations and promotions depend to an
inordinate degree on “productivity,” meaning the number of
citations issued. Partly as a consequence of City and FPD
priorities, many officers appear to see some residents,
especially those who live in Ferguson’s predominantly
African-American neighborhoods, less as constituents to be
protected than as potential offenders and sources of revenue.
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Together, the practice of aggressive, revenue-focused policing and the
subsequent financial consequences to poor citizens destroyed the
community’s trust in police. The report describes one seemingly minor
police interaction with devastating personal consequences.
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In the summer of 2012, a 32-year-old African-American man
sat in his car cooling off after playing basketball in a Ferguson
public park. An officer pulled up behind the man’s car,
blocking him in, and demanded the man’s Social Security
number and identification. Without any cause, the officer
accused the man of being a pedophile, referring to the
presence of children in the park, and ordered the man out of
his car for a pat-down, although the officer had no reason to
believe the man was armed. The officer also asked to search
the man’s car. The man objected, citing his constitutional
rights. In response, the officer arrested the man, reportedly at
gunpoint, charging him with eight violations of Ferguson’s
municipal code. One charge, Making a False Declaration, was
for initially providing the short form of his first name (e.g.,
“Mike” instead of “Michael”), and an address which, although
legitimate, was different from the one on his driver’s license.
Another charge was for not wearing a seat belt, even though
he was seated in a parked car. The officer also charged the
man both with having an expired operator’s license, and with
having no operator’s license in his possession. The man told
[DOJ investigators] that, because of these charges, he lost his
job as a contractor with the federal government that he had
held for years.
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The DOJ Ferguson report documents dozens of similar stories. After
reviewing tens of thousands of internal documents, officer emails, and
transcripts of hundreds of interviews, the DOJ report concluded that a system
of racially biased, petty harassment for citation dollars had replaced a
policing system focused on public safety. Change was recommended, and
community protestors demanded immediate action for a new type of policing.
A different systemic policing practice undermined community trust in New
York City. As the largest police force in the United States, NYPD regularly
draws scrutiny. But in a federal lawsuit over “stop and frisk” practices,
revelations of racial discrimination and systemic harassment came to light.
In a 2013 decision, Floyd et al. v. City of New York, Judge Shira Scheindlin
of the United States Court for the Southern District of New York held that the
NYPD’s stop-and-frisk practices violated the Constitution. Judge Scheindlin
observed that the NYPD’s informal goal to target “the right people, the right
time, the right location” became in practice discrimination against poor
people of color.
Judge Scheindlin’s findings revealed a demonstrated racial imbalance to
stop-and-frisk practices. Of the 4.4 million police stops conducted between
January 2004 and June 2012, 52% involved African Americans, 31% Latinos,
and 10% whites, even though as of 2010, the resident population was only
23% African American, 29% Latino, and 33% white. Of those stops,
contraband was recovered in only 1.8% of stops of African Americans, 1.7%
of Latinos, but 2.3% of whites. Weapons were seized in only 1.0% of stops
of African Americans, 1.1% of Latinos, but 1.4% of whites. Of all the stops,
only 6% resulted in an arrest and 6% in a summons, with 88% of police
contacts resulting in no further law enforcement action. Police used force in
those stops 23% of the time against African Americans, 24% against Latinos,
and 17% against whites. At the height of the stop-and-frisk program in New
York City, police conducted 686,000 stops a year, with the brunt of these
stops being felt by young men of color. Similar programs existed in other
big cities like Chicago, where the Chicago Police Department stopped
upward of 700,000 people in 2011.
In addition to the federal lawsuit, this policing practice led to major
protests in communities impacted by the stop-and-frisk strategy. It also drew
attention to the physical and psychological fear that such aggressive policing
had on communities of color. The Center for Constitutional Rights—a
nonprofit organization that helped lead the Floyd litigation—documented the
impact on citizens’ trust of police. Two quotes from interviews with
community members show the personal and societal impact of aggressive
stop-and-frisk practices:
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It makes you anxious about just being, walking around and
doing your daily thing while having a bunch of police always
there, always present and stopping people that look like me.
They say if you’re a young Black male, you’re more likely to
be stopped. So, it’s always this fear that “okay, this cop might
stop me,” for no reason, while I’m just sitting there in my
neighborhood.
—Joey M., a 25-year-old black man living in Central Harlem
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The sheer number of stops is actually ostracizing a huge
number of people who live in these communities that are
impacted by crime. It doesn’t make sense that you’re spending
so many man hours, so much energy and resources, to stop so
many innocent people and end up with very little output. The
number of guns that they found from the stops is extremely
small. So it just doesn’t seem effective.
—Manny W. (pseudonym), New York, New York
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These voices and hundreds like them changed the narrative of the stop-andfrisk practice from one of aggressive crime control to abusive social control.
Calls for reform altered the 2013 mayor’s race and ultimately led to a
reduction in aggressive stop-and-frisk practices (with no resulting increase in
crime).
Similar stories of systemic racial discrimination and individual abuse can
be told in other jurisdictions. Prior to the Ferguson report, the Department of
Justice had investigated and taken corrective action against unconstitutional
police practices in more than a dozen cities. DOJ has launched 68 significant
investigations into local policing practice over 20 years. Allegations of
excessive force, discriminatory practices, unlawful stops, and unlawful
arrests have drawn federal attention and concern. Major police departments
in Seattle, Cleveland, New Orleans, Albuquerque, and Newark had
demonstrated patterns and practices of unconstitutional policing. Other
police departments such as those in New York and Philadelphia fell under
court-monitored consent decrees as a result of civil rights lawsuits. After the
protests over the death of Freddie Gray, DOJ investigated the Baltimore
Police Department and found systemic unconstitutional practices involving
stops, frisks, arrests, and the use of force. The Baltimore DOJ report
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documents a pattern and practice of racially motived abuse of government
authority.
In the face of long-standing claims of discrimination, systemic problems,
and rekindled rage, police leaders began looking for new strategies to reorient
policing. The status quo had been exposed as racially biased and unfair, and a
new paradigm was needed to replace it. In response to demonstrated human
bias, it is not surprising that the lure of objective-seeming, data-driven
policing might be tempting. In addition, police administrators were being
pushed from the inside to change policing structures and provide line officers
more tools to do their jobs. Officers expressed anger at a failure to train,
support, or even acknowledge the difficult jobs police were expected to do
every day. Just as communities demanded change, so did police officers who
felt caught in a trap between community rage and bureaucratic neglect.
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Blue Lives Suffer
The backlash to the Black Lives Matter movement refocused attention on
policing and the danger of patrolling high-crime neighborhoods. Police
officers voiced frustration at staffing levels, training, and unrealistic
expectations when having to confront daily poverty, anger, and mental
illness. Police felt that their lives and the risks they took had been unfairly
devalued. After the tragic murder of five police officers protecting a
peaceful 2016 Black Lives Matters protest in Dallas, Texas, and then the
murder of three additional officers in Baton Rouge, Louisiana, a week later,
an equally powerful call was made to value police lives and the difficult job
they are called on to do.
Take, as an example, the daunting task to police Chicago, Illinois. The city
has approximately 100,000 gang members, 700 rival gangs, a homicide rate
in 2015 of 17 persons killed per 100,000 (the national average is 5 per
100,000), and certain districts with homicide rates triple the city average. In
August 2016, the city recorded 90 homicides with over 400 shootings. Yet,
since March 2013, over $190 million has been cut from the police budget.
This reduction meant training programs had to be cut, supervisory positions
ended, and rookie officers asked to police the most violent areas with the
least amount of training or support. Dropped off to patrol neighborhoods
they did not know and surrounded by residents who largely distrusted them,
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the rookies emphasized physical force and physical control to establish
authority. Neither side could see the other as merely responding to the
systemic failure to provide adequate resources for community-based policing.
Such ineffective policing strategies also led to internal frustration with police
administration. The lack of training and resources led officers to believe they
were set up to fail.
In similar fashion, after the death of Freddie Gray, Baltimore saw a
dramatic spike in homicides. Police officers publicly complained about the
impact of the protests on previously sanctioned aggressive tactics. Tasked to
fight an increase in crime but concerned that officers might not only be
disciplined but criminally prosecuted by their fellow law enforcement
brethren, officers felt abandoned. One Baltimore police lieutenant, Kenneth
Butler, was quoted as saying, “In 29 years, I’ve gone through some bad
times, but I’ve never seen it this bad. [Officers] feel as though the state’s
attorney will hang them out to dry. . . . I’m hearing it from guys who were
go-getters, who would go out here and get the guns and the bad guys and
drugs. They’re hands-off now. . . . I’ve never seen so many dejected faces.”
Nationally, police administrators sensed this growing frustration. The conflict
between black lives mattering and blue lives mattering undermined
established relationships and created calls for something new.
Despite these calls, the daily reality of policing did not change. Men and
women put on a uniform, entered a community, and saw all of the horror
humanity can offer. On an almost daily basis, police in urban areas witness
death, violence, and neglect. Predators hurt children, husbands hurt wives,
and children hurt each other. Blood. Bullets. Bruises. Rape. Anger. Fear.
Frustration. Mental illness. Addiction. Poverty. Despair. Every single shift.
As a professional class, police officers suffer this daily trauma without
sufficient mental health or social support. Police face frightening personal
experiences with their own physical safety at risk. Some studies suggest that
one-third to one-eighth of retired and active-duty police officers suffer from
posttraumatic stress disorder (PTSD) as a result of their jobs. This
unaddressed trauma, fear, and daily stress burdens officers who are required
to respond every shift to a new emergency.
And police administrators still had to fill those shifts. Police administrators
needed to do more with fewer officers. Demands for new tools increased, as
did the demand for “smarter policing.” Technology, as it has in other areas of
society, became the answer to growing officer complaints and financial
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constraints. Technology promised to make hard jobs easier, so administrators
and line officers all bought in, hoping for the best. Big data and predictive
tools offered the chance to change the reality on the ground, but more
importantly, they offered some hope that change was possible.
Responding to Crisis with Innovation
Out of the tension of black lives’ frustration with police officers and blue
lives’ frustration with police administration, the lure of technology to add
objectivity to policing and to do more with less beg…