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 ( )

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. ( )