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The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement
Winner, 2018 Law & Legal Studies PROSE Award
The consequences of big data and algorithm-driven policing and its impact on law enforcement
In a high-tech command center in downtown Los Angeles, a digital map lights up with 911 calls, television monitors track breaking news stories, surveillance cameras sweep the streets, and rows of networked computers link analysts and police officers to a wealth of law enforcement intelligence.
This is just a glimpse into a future where software predicts future crimes, algorithms generate virtual “most-wanted” lists, and databanks collect personal and biometric information. The Rise of Big Data Policing introduces the cutting-edge technology that is changing how the police do their jobs and shows why it is more important than ever that citizens understand the far-reaching consequences of big data surveillance as a law enforcement tool.
Andrew Guthrie Ferguson reveals how these new technologies ―viewed as race-neutral and objective―have been eagerly adopted by police departments hoping to distance themselves from claims of racial bias and unconstitutional practices. After a series of high-profile police shootings and federal investigations into systemic police misconduct, and in an era of law enforcement budget cutbacks, data-driven policing has been billed as a way to “turn the page” on racial bias.
But behind the data are real people, and difficult questions remain about racial discrimination and the potential to distort constitutional protections.
In this first book on big data policing, Ferguson offers an examination of how new technologies will alter the who, where, when and how we police. These new technologies also offer data-driven methods to improve police accountability and to remedy the underlying socio-economic risk factors that encourage crime.
The Rise of Big Data Policing is a must read for anyone concerned with how technology will revolutionize law enforcement and its potential threat to the security, privacy, and constitutional rights of citizens.
Read an excerpt and interview with Andrew Guthrie Ferguson in The Economist.
The consequences of big data and algorithm-driven policing and its impact on law enforcement
In a high-tech command center in downtown Los Angeles, a digital map lights up with 911 calls, television monitors track breaking news stories, surveillance cameras sweep the streets, and rows of networked computers link analysts and police officers to a wealth of law enforcement intelligence.
This is just a glimpse into a future where software predicts future crimes, algorithms generate virtual “most-wanted” lists, and databanks collect personal and biometric information. The Rise of Big Data Policing introduces the cutting-edge technology that is changing how the police do their jobs and shows why it is more important than ever that citizens understand the far-reaching consequences of big data surveillance as a law enforcement tool.
Andrew Guthrie Ferguson reveals how these new technologies ―viewed as race-neutral and objective―have been eagerly adopted by police departments hoping to distance themselves from claims of racial bias and unconstitutional practices. After a series of high-profile police shootings and federal investigations into systemic police misconduct, and in an era of law enforcement budget cutbacks, data-driven policing has been billed as a way to “turn the page” on racial bias.
But behind the data are real people, and difficult questions remain about racial discrimination and the potential to distort constitutional protections.
In this first book on big data policing, Ferguson offers an examination of how new technologies will alter the who, where, when and how we police. These new technologies also offer data-driven methods to improve police accountability and to remedy the underlying socio-economic risk factors that encourage crime.
The Rise of Big Data Policing is a must read for anyone concerned with how technology will revolutionize law enforcement and its potential threat to the security, privacy, and constitutional rights of citizens.
Read an excerpt and interview with Andrew Guthrie Ferguson in The Economist.
272 pages, Paperback
First published October 3, 2017
57 people are currently reading
691 people want to read
About the author
Andrew Guthrie Ferguson
5 books11 followersAndrew Guthrie Ferguson is an Assistant Professor of Law at the University of the District of Columbia's David A. Clarke School of Law. Professor Ferguson teaches and writes in the area of criminal law, criminal procedure, and evidence.
Prior to joining the law faculty, Professor Ferguson worked as a supervising attorney at the Public Defender Service for the District of Columbia. Professor Ferguson is also involved in developing constitutional education projects in the Washington D.C. area.
Prior to joining the law faculty, Professor Ferguson worked as a supervising attorney at the Public Defender Service for the District of Columbia. Professor Ferguson is also involved in developing constitutional education projects in the Washington D.C. area.
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Displaying 1 - 23 of 23 reviews
August 27, 2018
The central problem of this book seems to be that Ferguson seems to think that All Cops Are NOT Bastards.
Bad: Argument that predictive analytics can be used to identify police misconduct is naive, and underestimates the political capital of police unions. This argument also claims that the adoption of big data policing is part of a larger effort to "turn the page" on racial discrimination by police. Research by most scholars in this area would argue that the primary motive is financial efficiency, and has nothing to do with overcoming racist behaviour by police.
Good: Useful, simple overview of the big data tech that underpin predictive policing systems. Interesting, and comprehensive connections to U.S. privacy law.
Bad: Argument that predictive analytics can be used to identify police misconduct is naive, and underestimates the political capital of police unions. This argument also claims that the adoption of big data policing is part of a larger effort to "turn the page" on racial discrimination by police. Research by most scholars in this area would argue that the primary motive is financial efficiency, and has nothing to do with overcoming racist behaviour by police.
Good: Useful, simple overview of the big data tech that underpin predictive policing systems. Interesting, and comprehensive connections to U.S. privacy law.
October 8, 2020
The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement by Andrew Guthrie Ferguson is a study of how big data is and can be used by law enforcement to encroach on what used to be privacy. Ferguson is an Assistant Professor of Law at the University of the District of Columbia’s David A. Clarke School of Law. Professor Ferguson teaches and writes in the area of criminal law, criminal procedure, and evidence.
American’s have always enjoyed privacy. Constitutional amendments like the Fourth and Fourteenth keep government intrusions at bay for most people. The internet and mobile communications have changed all of that. We gladly give personal information away to web sites. Sites like Facebook not only have your personal information but they know who your friends are, places you check into, who and what you like, where you live, and pictures you take. Other web sites collect information items you purchase and also looked for. A Russian photographer has recently used a facial recognition application to find out information about strangers on the Metro. Simply taking a picture of a person on the Metro the photographer is able to identify that person through their social media accounts. Our private lives have become very public in the age of Big Data. Companies mine and buy this data for their own purposes. Say, for example, you owned a motorcycle shop and wanted more customers. You can pay a data collection company for personal information about everyone with a motorcycle license in your area. You would then have a contact list of potential customers. Information is still power in today’s world.
There have been news stories of people posting pictures of themselves on social media sites with automatic weapons, drugs, or taking part in illegal activities. Police have used these postings to arrested people. Social media postings do not have an expectation of privacy; what you post is essentially public. The Los Angeles Police Department, with outside help, tracks and records all crime and creates a database and an active map that predicts where and when crimes occur. The idea is to police a predicted area before a crime happens — actual crime prevention. NYC Police use cameras on the roads and sidewalk and can actively look for suspicious activity as well as possibly identify the criminal. These systems don’t seem to infringe on people’s rights. One does not have an expectation of privacy when in a public place.
In Chicago, an algorithm is used to help predict those who might commit a crime or become a victim of a crime. A list is made and police visit those people on the list and deliver a “we are watching you a message.” What happens when the algorithm is wrong is another thing. People without a criminal record or any other indicators might come up on the list because of a friend or relative who was killed. It’s not a perfect system but Chicago police rate it well. 70% of those shot were on the list and well as 80% of those arrested in shootings. Still, there seems to be no real infringement on individuals rights. Police use public data to predict crime and criminals
The problem comes in when the results of the Big Data seem to be the same as those in racial profiling. The highest crime areas are usually in the inner city and areas where the minority population is high. The Chicago list targets gang members 95% are African-American or Latino. Can Big Data just be another means of racial profiling? Ferguson looks at racial bias in Big Data and researches whether the data is biased, the system is biased, or if the data is correct. Ferguson also discusses the constitutionality of using Big Data as probable cause instead of “gut instinct.”
Where does law enforcement and Big Data limit themselves? Imagine if your local police force bought personal data from Google or Facebook. Private information becomes public information, becomes building blocks for private and government databases as Ferguson explains. A warrant is not needed for public information. Police gather public information all the time. License plate readers not only verify if the plates are good but also track and store all the locations where that plate has been seen. The police could, in time, track your daily routine. Upgrades to police body cams will have facial recognition software. One may not be required to identify themselves, but facial recognition will allow the police to identify a person anyway.
Interestingly there is a push by law enforcement to use Big Data and other monitoring; however, requirements for police to wear and use body cameras meets resistance by police who do not want their every action recorded while on duty. Similar algorithms used by police to monitor and predict crime could also be used to monitor police officers. Just like a small percentage of the population is responsible for the majority of the crime, a small percentage of police are responsible for the majority of the complaints. Big Data could help identify bad cops.
Presently, we willing give our data to Amazon, social media, mobile providers (location tracking, calls, and texts), and search engines. Walmart collects 2.5 million gigabytes every hour from its customers enough to 50 million, four drawer filing cabinets with information every hour. The government is also collecting data. Perhaps the most extensive is the Post Office’s Mail Isolation Control and Tracking program. It photographs every piece of mail. Your name, address, and the sender is recorded on every piece of your mail. Big Data could also be used by the police and other community services by identifying runaways, homeless, Amber Alert victims, and Silver Alert victims. There is good that can come from Big Data if it is used correctly. In the wrong hands, it could create tyranny. The Rise of Big Data Policing is a timely and possibly frightening book as what was formerly conspiracy theories become our daily reality.
Available October 3, 2017, from NYU Press
American’s have always enjoyed privacy. Constitutional amendments like the Fourth and Fourteenth keep government intrusions at bay for most people. The internet and mobile communications have changed all of that. We gladly give personal information away to web sites. Sites like Facebook not only have your personal information but they know who your friends are, places you check into, who and what you like, where you live, and pictures you take. Other web sites collect information items you purchase and also looked for. A Russian photographer has recently used a facial recognition application to find out information about strangers on the Metro. Simply taking a picture of a person on the Metro the photographer is able to identify that person through their social media accounts. Our private lives have become very public in the age of Big Data. Companies mine and buy this data for their own purposes. Say, for example, you owned a motorcycle shop and wanted more customers. You can pay a data collection company for personal information about everyone with a motorcycle license in your area. You would then have a contact list of potential customers. Information is still power in today’s world.
There have been news stories of people posting pictures of themselves on social media sites with automatic weapons, drugs, or taking part in illegal activities. Police have used these postings to arrested people. Social media postings do not have an expectation of privacy; what you post is essentially public. The Los Angeles Police Department, with outside help, tracks and records all crime and creates a database and an active map that predicts where and when crimes occur. The idea is to police a predicted area before a crime happens — actual crime prevention. NYC Police use cameras on the roads and sidewalk and can actively look for suspicious activity as well as possibly identify the criminal. These systems don’t seem to infringe on people’s rights. One does not have an expectation of privacy when in a public place.
In Chicago, an algorithm is used to help predict those who might commit a crime or become a victim of a crime. A list is made and police visit those people on the list and deliver a “we are watching you a message.” What happens when the algorithm is wrong is another thing. People without a criminal record or any other indicators might come up on the list because of a friend or relative who was killed. It’s not a perfect system but Chicago police rate it well. 70% of those shot were on the list and well as 80% of those arrested in shootings. Still, there seems to be no real infringement on individuals rights. Police use public data to predict crime and criminals
The problem comes in when the results of the Big Data seem to be the same as those in racial profiling. The highest crime areas are usually in the inner city and areas where the minority population is high. The Chicago list targets gang members 95% are African-American or Latino. Can Big Data just be another means of racial profiling? Ferguson looks at racial bias in Big Data and researches whether the data is biased, the system is biased, or if the data is correct. Ferguson also discusses the constitutionality of using Big Data as probable cause instead of “gut instinct.”
Where does law enforcement and Big Data limit themselves? Imagine if your local police force bought personal data from Google or Facebook. Private information becomes public information, becomes building blocks for private and government databases as Ferguson explains. A warrant is not needed for public information. Police gather public information all the time. License plate readers not only verify if the plates are good but also track and store all the locations where that plate has been seen. The police could, in time, track your daily routine. Upgrades to police body cams will have facial recognition software. One may not be required to identify themselves, but facial recognition will allow the police to identify a person anyway.
Interestingly there is a push by law enforcement to use Big Data and other monitoring; however, requirements for police to wear and use body cameras meets resistance by police who do not want their every action recorded while on duty. Similar algorithms used by police to monitor and predict crime could also be used to monitor police officers. Just like a small percentage of the population is responsible for the majority of the crime, a small percentage of police are responsible for the majority of the complaints. Big Data could help identify bad cops.
Presently, we willing give our data to Amazon, social media, mobile providers (location tracking, calls, and texts), and search engines. Walmart collects 2.5 million gigabytes every hour from its customers enough to 50 million, four drawer filing cabinets with information every hour. The government is also collecting data. Perhaps the most extensive is the Post Office’s Mail Isolation Control and Tracking program. It photographs every piece of mail. Your name, address, and the sender is recorded on every piece of your mail. Big Data could also be used by the police and other community services by identifying runaways, homeless, Amber Alert victims, and Silver Alert victims. There is good that can come from Big Data if it is used correctly. In the wrong hands, it could create tyranny. The Rise of Big Data Policing is a timely and possibly frightening book as what was formerly conspiracy theories become our daily reality.
Available October 3, 2017, from NYU Press
September 5, 2019
Ferguson explains the effects of big data policing well, but wow, he has a lot of optimism regarding police culture and willingness to change the status quo. I practically laughed thought the "Bright Data" chapter because no local government is going to look at what data predictive policing can provide and make the decision to reinvest in social services. That's my cynicism, but that it's proposed so innocently without acknowledging its unlikelihood was funny. Also, I kept getting the feeling that Ferguson understands the movement for Black lives but only says what he has to in order to please editors. I don't buy that he has genuine concerns about how devastating predictive policing is for Black communities. Maybe that's not fair, but why was his example of video surveillance disproving hearsay the one about Michael Brown's hands being raised?? Seems like he's chosen sides 👎
November 1, 2018
Unfortunately in a tome about predictive policing and "big data" in the American policing services, there is no mention of the Jim Crow laws or the New Jim Crow. There was also very little reference to the scientists who "fathered" the tools of big data. The format of the book bounces back and forth between "Blue" and Black lives, with very little ethical discourse surrounding the historical events that have positioned these two (somehow antonymous) communities as oppositional.
August 22, 2020
I am now at the end and here is what I have to say:
I am reinstating points one and two: That Prof Ferguson is too big a techno-vangelist, too many blind spots as far as the dark side of big data surveillance. I also re-assert that the data police are collecting might be better off in community-based organizations.
My point of view is that we need less people in jail and prison, and more people in good jobs. Some of this is informed by my being a victim of burglary a few years ago: A guy came in through our fire escape window, stole an old laptop and a few other not very valuable things. They caught him, I went to court to testify against him, and even though he was acquitted on my burglary, he was convicted of several others with the same MO, and sent to prison for a long long time. Hearing this, I couldn't help think: "What a waste of life." All this for our old laptop that I can't imagine would be worth that much to fencers, and which we could afford to replace. Meanwhile, we the tax payers are paying a lot of money to pay for his imprisonment, and he's been totally institutionalized, and contributing nothing to society. What a waste!
This leads into a discussion on inequality, and how some fall into crime as a life style when they so little to lose. But I don't want to go there - too big to get into. The other point I really feel obliged to make is this:
The 20th century was filled with governments spying, "disappearing", killing, and otherwise removing on their own citizens. McCarthy, Russia, East Germany, China... China right now is taking this to the next level (https://www.theatlantic.com/magazine/...) as well as selling this abroad to other countries. This is a terrible thing, not just for democracy, but for humanity. To have no privacy and be under constant surveillance is where humanity goes to die.
You think that the data that is being collected by the police is going to locked up and only be available to the police? Get real! Criminals, international cyber-warriors, and corrupt politicians are 100% eventually going to use this for there own purposes. You should be worried. Very worried.
My review is 3 stars, because honestly I don't know how to rate this book based on a linear scale like this. You should read the book, not to endorse what is happening, but rather to fight back while we still can
-- Edit --
I wrote the review below before finishing the book: The way the author begins by talking about all the wonderful things that big data promises to bring to policing rubbed me the wrong way and pushed my techno-skeptic buttons. The author does actually include serious considerations of the drawbacks to big data policing... I will update this again when I have finished the book.
--
Issues I have with this book:
1. The author is a silicon valley style techno-vangelist: "Technology will make so many things better!" In reality, this is a very questionable default position to have with unproven technology. The claims that using big data will solve problems of bias, racism, and inefficient policing need to be backed up not theoretically, but with actual impartial analysis
2. The kind of data collection that the author talks about would be (in my opinion) better off in the hands of community-based organizations. As the author acknowledges in the book, communities and police in the US are often suspicious and oppositional: Giving money and resources for this kind of technology to Community Based Organizations (CBOs) who could then make the decision to share with the police if/when necessary would be a better approach. Policing in the US should be re-imagined: Police administrators are open to admitting that police are put into too many situations that they are not equipped for.
3. In the best case, "Better policing" could help police be smarter about handling crime, but it DOES NOT ADDRESS THE SOCIAL ISSUES behind the crimes themselves. To raise communities of color up, empower these communities and defund the police. This is a proven, working strategy
I am reinstating points one and two: That Prof Ferguson is too big a techno-vangelist, too many blind spots as far as the dark side of big data surveillance. I also re-assert that the data police are collecting might be better off in community-based organizations.
My point of view is that we need less people in jail and prison, and more people in good jobs. Some of this is informed by my being a victim of burglary a few years ago: A guy came in through our fire escape window, stole an old laptop and a few other not very valuable things. They caught him, I went to court to testify against him, and even though he was acquitted on my burglary, he was convicted of several others with the same MO, and sent to prison for a long long time. Hearing this, I couldn't help think: "What a waste of life." All this for our old laptop that I can't imagine would be worth that much to fencers, and which we could afford to replace. Meanwhile, we the tax payers are paying a lot of money to pay for his imprisonment, and he's been totally institutionalized, and contributing nothing to society. What a waste!
This leads into a discussion on inequality, and how some fall into crime as a life style when they so little to lose. But I don't want to go there - too big to get into. The other point I really feel obliged to make is this:
The 20th century was filled with governments spying, "disappearing", killing, and otherwise removing on their own citizens. McCarthy, Russia, East Germany, China... China right now is taking this to the next level (https://www.theatlantic.com/magazine/...) as well as selling this abroad to other countries. This is a terrible thing, not just for democracy, but for humanity. To have no privacy and be under constant surveillance is where humanity goes to die.
You think that the data that is being collected by the police is going to locked up and only be available to the police? Get real! Criminals, international cyber-warriors, and corrupt politicians are 100% eventually going to use this for there own purposes. You should be worried. Very worried.
My review is 3 stars, because honestly I don't know how to rate this book based on a linear scale like this. You should read the book, not to endorse what is happening, but rather to fight back while we still can
-- Edit --
I wrote the review below before finishing the book: The way the author begins by talking about all the wonderful things that big data promises to bring to policing rubbed me the wrong way and pushed my techno-skeptic buttons. The author does actually include serious considerations of the drawbacks to big data policing... I will update this again when I have finished the book.
--
Issues I have with this book:
1. The author is a silicon valley style techno-vangelist: "Technology will make so many things better!" In reality, this is a very questionable default position to have with unproven technology. The claims that using big data will solve problems of bias, racism, and inefficient policing need to be backed up not theoretically, but with actual impartial analysis
2. The kind of data collection that the author talks about would be (in my opinion) better off in the hands of community-based organizations. As the author acknowledges in the book, communities and police in the US are often suspicious and oppositional: Giving money and resources for this kind of technology to Community Based Organizations (CBOs) who could then make the decision to share with the police if/when necessary would be a better approach. Policing in the US should be re-imagined: Police administrators are open to admitting that police are put into too many situations that they are not equipped for.
3. In the best case, "Better policing" could help police be smarter about handling crime, but it DOES NOT ADDRESS THE SOCIAL ISSUES behind the crimes themselves. To raise communities of color up, empower these communities and defund the police. This is a proven, working strategy
March 5, 2018
Law enforcement is a tough task. There are many more questions than answers here. Read carefully and don't sleep.
The author presents a thoughtful and even-handed review of considerations to evaluate when considering data-driven strategies for law-enforcement. He advocates active participation by communities, politicians and police chiefs before setting new policy and methods in place to take advantage of technologies and observations based on collections of big data. He draws attention to inherent weaknesses in the promise and in the implementation of new systems while, at the same time, recognizing potential benefits and motivating factors.
The sources of data are out there and continue to grow. The processing power and algorithms continue to develop at breakneck speed and parallel the explosion of technology growth in commerce and government. The author has carefully examined the settings and posed the questions. The answers require serious consideration by those who make law-enforcement policy, by those who fund law-enforcement policy and by those who are affected by law-enforcement policy (which is just about everyone).
The hard part here is that the material is quite technical and detailed in nature. The consequences are far-reaching and can dis-proportionally affect poor people, people of color and police-community relations if not handled properly. The author talks about how 'blue data' can help to hold public administration accountable and how annual process-improvement forums can be used to develop better policy with input from many points of view. He stresses the importance of testing and accountability in implementing policy changes.
There is a lot to digest here but that is no excuse for ignoring the growth of this phenomenon. If we turn a blind eye because the considerations ares complex, the less-informed leaders of law-enforcement will likely proceed anyway because of the promise of big data systems to deflect many charges of discrimination and abuse of power by the police. The challenges are equally tough for the lawyers and privacy advocates as they are for the social workers who would find opportunities to help those at risk find ways to avert the situations which call for law enforcement.
The author presents a thoughtful and even-handed review of considerations to evaluate when considering data-driven strategies for law-enforcement. He advocates active participation by communities, politicians and police chiefs before setting new policy and methods in place to take advantage of technologies and observations based on collections of big data. He draws attention to inherent weaknesses in the promise and in the implementation of new systems while, at the same time, recognizing potential benefits and motivating factors.
The sources of data are out there and continue to grow. The processing power and algorithms continue to develop at breakneck speed and parallel the explosion of technology growth in commerce and government. The author has carefully examined the settings and posed the questions. The answers require serious consideration by those who make law-enforcement policy, by those who fund law-enforcement policy and by those who are affected by law-enforcement policy (which is just about everyone).
The hard part here is that the material is quite technical and detailed in nature. The consequences are far-reaching and can dis-proportionally affect poor people, people of color and police-community relations if not handled properly. The author talks about how 'blue data' can help to hold public administration accountable and how annual process-improvement forums can be used to develop better policy with input from many points of view. He stresses the importance of testing and accountability in implementing policy changes.
There is a lot to digest here but that is no excuse for ignoring the growth of this phenomenon. If we turn a blind eye because the considerations ares complex, the less-informed leaders of law-enforcement will likely proceed anyway because of the promise of big data systems to deflect many charges of discrimination and abuse of power by the police. The challenges are equally tough for the lawyers and privacy advocates as they are for the social workers who would find opportunities to help those at risk find ways to avert the situations which call for law enforcement.
December 18, 2017
Big Data brings a lot of challenges and benefits to business and the world is seemingly focussing on these presently, but equally law enforcement is engaged with big data although so-far it has escaped a lot of scrutiny, other than the age-old privacy and ‘Big Brother’-type warnings.
This is a fascinating book that looks at Big Data from a law enforcement perspective, considering the impact and benefits it is bringing as well as examining the risks and areas that need to be considered. It can also be considered on a tangent, focussing on political, sociological and public interests too. Big Data is developing and what may be possible in the future can eclipse what is present today – this cannot be viewed in isolation. The author has done a great job in bringing all the issues together.
A lot of Big Data-related initiatives are being coached as benefits by virtue of being race-neutral, wholly objective and not possibly discriminatory. Others may suggest that the ‘machines’ only react to the way they are processed and, of course, while it requires humans to do the last-leg bit of policing the ‘human imperative’ and its possible biases can be in play, with blind eyes deployed as necessary. Both elements can be valid, of course, although should the data and its processing be entirely free of bias, society may not be ready for some of the revelations that can emerge.
The book takes a hard look at the technologies that can affect and alter the who, where, when and society is policed. It is written from a U.S.-perspective although the same issues will exist in other countries. It was very engaging, well-written, accessible and quite impartial. Excellent referencing is used throughout to aid future research and study.
Some of the issues may be viewed as being alarming and you must hope that sufficient control can be deployed, yet many of the benefits can also be applauded and welcomed. With careful reading, consideration and implementation, it is quite possible that those interested in non-policing issues can derive other benefits for their own industry interests too.
This is a fascinating book that looks at Big Data from a law enforcement perspective, considering the impact and benefits it is bringing as well as examining the risks and areas that need to be considered. It can also be considered on a tangent, focussing on political, sociological and public interests too. Big Data is developing and what may be possible in the future can eclipse what is present today – this cannot be viewed in isolation. The author has done a great job in bringing all the issues together.
A lot of Big Data-related initiatives are being coached as benefits by virtue of being race-neutral, wholly objective and not possibly discriminatory. Others may suggest that the ‘machines’ only react to the way they are processed and, of course, while it requires humans to do the last-leg bit of policing the ‘human imperative’ and its possible biases can be in play, with blind eyes deployed as necessary. Both elements can be valid, of course, although should the data and its processing be entirely free of bias, society may not be ready for some of the revelations that can emerge.
The book takes a hard look at the technologies that can affect and alter the who, where, when and society is policed. It is written from a U.S.-perspective although the same issues will exist in other countries. It was very engaging, well-written, accessible and quite impartial. Excellent referencing is used throughout to aid future research and study.
Some of the issues may be viewed as being alarming and you must hope that sufficient control can be deployed, yet many of the benefits can also be applauded and welcomed. With careful reading, consideration and implementation, it is quite possible that those interested in non-policing issues can derive other benefits for their own industry interests too.
March 5, 2018
From community members, to police officials, to business and academic professionals, this book serves both as a ready-reference to the technical aspects of big-data and invokes critical thinking about where big-data fits into our everyday lives and how both business and government utilize it.
Big-data is being captured all around us; big-data has captured this book review and the fact that you've read it; big-data just tracked the mobile device you're reading from, big-data is permanent, and encroaching into our daily lives regardless of our willingness to participate.
Ferguson does an exceptional job at not only raising awareness of big-data's pervasiveness but encourages a national conversation into how it's used. There is a balance between privacy, due process and the government's interest in keeping us safe. However, the nation's privacy laws haven't kept pace with technology. By merely purchasing it, government, especially law enforcement, can obtain inordinate amounts of private information on each and everyone one of us. Absent updated laws, policies, and procedures, what are business and law enforcement agencies' ethical and moral responsibilities in obtaining, disseminating, procuring, and utilizing big-data? Also, is the use of big-data in policing hurting and promoting bias in our communities of color?
These questions and more are explored in Ferguson's exploration into big-data's uses and the future implications of such. We recommend this book as an educational and critical-thought piece.
Big-data is being captured all around us; big-data has captured this book review and the fact that you've read it; big-data just tracked the mobile device you're reading from, big-data is permanent, and encroaching into our daily lives regardless of our willingness to participate.
Ferguson does an exceptional job at not only raising awareness of big-data's pervasiveness but encourages a national conversation into how it's used. There is a balance between privacy, due process and the government's interest in keeping us safe. However, the nation's privacy laws haven't kept pace with technology. By merely purchasing it, government, especially law enforcement, can obtain inordinate amounts of private information on each and everyone one of us. Absent updated laws, policies, and procedures, what are business and law enforcement agencies' ethical and moral responsibilities in obtaining, disseminating, procuring, and utilizing big-data? Also, is the use of big-data in policing hurting and promoting bias in our communities of color?
These questions and more are explored in Ferguson's exploration into big-data's uses and the future implications of such. We recommend this book as an educational and critical-thought piece.
October 17, 2017
Review originally published at Book of Bogan.
Big data has become an integral part of our lives, whether we are aware of it or not. With its use in a range of industries, including public safety, as a society we would do well to keep ourselves informed at what we are doing, and allowing to be done in the name of security and safety. This book examines the use, and potential misuse of big data projects in the application of the law in society.
It is definitely written with a particular slant in mind, to show that there is an inherent bias in the way the data is being applied. From an outside perspective, I felt like it was a bit of a chicken and egg situation, but when you are asking a black box questions, and not examining the underlying conclusions it draws, there will inevitably be problems. This is still a relatively new field, and I think caution should be exercised whenever a human is being removed from the decision-making process.
The Rise of Big Data Policing is a well-researched, and well-written book that will be a worthwhile read for anyone with an interest in law enforcement, big data or privacy.
I received a review copy from the publisher through NetGalley in exchange for an honest review.
Big data has become an integral part of our lives, whether we are aware of it or not. With its use in a range of industries, including public safety, as a society we would do well to keep ourselves informed at what we are doing, and allowing to be done in the name of security and safety. This book examines the use, and potential misuse of big data projects in the application of the law in society.
It is definitely written with a particular slant in mind, to show that there is an inherent bias in the way the data is being applied. From an outside perspective, I felt like it was a bit of a chicken and egg situation, but when you are asking a black box questions, and not examining the underlying conclusions it draws, there will inevitably be problems. This is still a relatively new field, and I think caution should be exercised whenever a human is being removed from the decision-making process.
The Rise of Big Data Policing is a well-researched, and well-written book that will be a worthwhile read for anyone with an interest in law enforcement, big data or privacy.
I received a review copy from the publisher through NetGalley in exchange for an honest review.
October 2, 2018
Some things about this book:
1. Ferguson recognizes and contemplates the constitutional and civil rights issues with police gathering vast amounts of data on us.
2. Ferguson recognizes that with big data comes a much more individualized look at social issues and that those issues do not have to be solved solely by law enforcement.
3. Ferguson clearly explicates and articulates some of the most recent and most popular uses of big data gathering tech used by police without getting lost in the technical details.
4. Ferguson raises some really, really important questions around our rights to be free from arbitrary search, seizure, and imprisonment based on algorithms that use bad/biased police data. Bad data in, produces bad data out.
5. His audience seems to be policy makers, decision makers, and police administrators.
6. He's good at straddling the line between what constitutional advocates and social justice activists want to hear and what the police want to hear.
7. Worth the read. But don't get lost trying to justify it. Read other works that review issues of big data gathering. They offer support to his arguments.
8. Last, well written and recommended.
1. Ferguson recognizes and contemplates the constitutional and civil rights issues with police gathering vast amounts of data on us.
2. Ferguson recognizes that with big data comes a much more individualized look at social issues and that those issues do not have to be solved solely by law enforcement.
3. Ferguson clearly explicates and articulates some of the most recent and most popular uses of big data gathering tech used by police without getting lost in the technical details.
4. Ferguson raises some really, really important questions around our rights to be free from arbitrary search, seizure, and imprisonment based on algorithms that use bad/biased police data. Bad data in, produces bad data out.
5. His audience seems to be policy makers, decision makers, and police administrators.
6. He's good at straddling the line between what constitutional advocates and social justice activists want to hear and what the police want to hear.
7. Worth the read. But don't get lost trying to justify it. Read other works that review issues of big data gathering. They offer support to his arguments.
8. Last, well written and recommended.
Read
February 29, 2020This is a very organized and informative introduction to the different types of data-reliant policing technologies available, such as: person-based data policing or 'heat map' policing that predicts high-risk individuals through a combination of several risk factors such as recent experiences with high-intensity violence; place-based policing or hotspot-prediction policing that predicts crime-vulnerable environments; continuous policing and monitoring through technologies such as street-placed facial recognition and license-plate photography. The book tries to be balanced in presenting both the rationales for and arguments against data-driven policing, presenting the sides and perspectives of both police officers, and civil liberties groups and communities of minorities.
This book is very important as an eye-opening study on a rising phenomenon in police practice that not a lot of us might be aware of in-depth.
This book is very important as an eye-opening study on a rising phenomenon in police practice that not a lot of us might be aware of in-depth.
October 15, 2017
Great opener soon gives way to repetitive points on algorithims. The elements on social media and optics in general is interesting, but the telling is too staid to hold this layperson's interest for long. The author has many criticisms for law enforcement, but offers few solutions. The narrative too one sided to be partial to be neutral or informative. If you're looking into data as resource for government, business, or individuals you may not find what you're looking for as this book is more of a long opinion piece.
August 26, 2018
The author offered a critical point: big data policing does not offer remedies. Data gives us insights but how to deal with it is entirely up to us, not just the police forces. Removing a criminal from society does not mean communities are clear of the poison. Effective strategies to combat crimes or public problems remain a struggle to everyone involved.
February 7, 2019
Well-researched with several examples from real police forces around the country. A necessary handbook for data nerds working with criminal data. It asks very essential questions about bias, validity, and what is measurable. It also points out what's missing/ data gaps and why. It's a wonky read but a good one.
December 19, 2025
This book provides a valuable insight into the digital world of policing, and the potential downsides of some of the recent innovations in the field. However, the author starts repeating himself after the first four chapters and there really is no point in finishing the book completely. This reads more like a prolonged thesis than a well thought out book.
January 25, 2023
This book shows a lot of insight into past and recent technologies in big data to achieve effective policing. It also demonstrates the disadvantages of these technologies and the threat of relying solely upon big data to solve the most complex problems.
July 17, 2024
Ehhhh he is just a bit too hardcore of a technosolutionist. He sort of acknowledges issues but then just pushes through them. I feel like it lacked a critical bite.
But it was helpful for my own thinking as a starting off point or as a way of wanting to critique this style of thinking.
August 8, 2020
A well written primer on the use of big data technology in policing. Masterfully describes both the potential utilities and risks. A must for anyone with an interest in both the law and technology.
April 1, 2022
Waffling between admiration for the power of data and caution against the implications of its use without transparency and accountability, this book creeped the hell out of me.
August 2, 2024
Very thorough book regarding the pros and cons of different programs and algorithms. However it gets really repetitive.
Read
April 28, 2023«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 from 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 data to law enforcement. Phone numbers, emails, and finances can all be studied for suspicious links. Government agencies collect health, educational, and crime records. Detectives monitor public Facebook, YouTube, and Twitter feeds. Aggregating data centers sort and study the accumulated information in local and federally funded fusion centers. This is the big data world of law enforcement – still largely in its infancy but offering vastly more incriminating bits of data to use and study.»
«This book grapples with these big decisions about new technology, policing strategy, and its community impacts. It has raised a largely hidden issue at the core of big data policing – the problem of «black data» - with all the complications of race, accountability, and constitutional law that come along with it. It has also examined how police practice and societal needs can be illumined by big data technologies.»
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 from 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 data to law enforcement. Phone numbers, emails, and finances can all be studied for suspicious links. Government agencies collect health, educational, and crime records. Detectives monitor public Facebook, YouTube, and Twitter feeds. Aggregating data centers sort and study the accumulated information in local and federally funded fusion centers. This is the big data world of law enforcement – still largely in its infancy but offering vastly more incriminating bits of data to use and study.»
«This book grapples with these big decisions about new technology, policing strategy, and its community impacts. It has raised a largely hidden issue at the core of big data policing – the problem of «black data» - with all the complications of race, accountability, and constitutional law that come along with it. It has also examined how police practice and societal needs can be illumined by big data technologies.»
Want to read
September 24, 2017Note: I accessed a digital review copy of this book through Edelweiss.
February 26, 2018
Racial tensions have been seriously on the rise - especially where police are involved. Computers are touted as being unfeeling, data crunching, machines. Which could be their greatest asset and greatest downfall. Except, we don't really trust machines to do everything without oversight, and that oversight is completely human. Which means that there's still human bias coming into play. This tackles that question head-on.
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