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The expansion of digital record keeping by police departments across the U.S. in the 1990s ushered in the era of data-driven policing. Huge metropolises like New York City crunched reams of crime and arrest data to find and target “hot spots” for extra policing. Researchers at the time found that this reduced crime without necessarily displacing it to other parts of the city—although some of the tactics used, such as stop-and-frisk, were ultimately criticized by a federal judge, among others, as civil rights abuses.

The next development in data-informed policing was ripped from the pages of science fiction: software that promised to take a jumble of local crime data and spit out accurate forecasts of where criminals are likely to strike next, promising to stop crime in its tracks. One of the first, and reportedly most widely used, is PredPol, its name an amalgamation of the words “predictive policing.” The software, derived from an algorithm used to predict earthquake aftershocks, was developed by professors at UCLA and released in 2011. By sending officers to patrol these algorithmically predicted hot spots, these programs promise they will deter illegal behavior.

But law enforcement critics had their own prediction: that the algorithms would send cops to patrol the same neighborhoods they say police always have, those populated by people of color. Because the software relies on past crime data, they said, it would reproduce police departments’ ingrained patterns and perpetuate racial injustice, covering it with a veneer of objective, data-driven science.

On today’s show we discuss the gunshot detection system ShotSpotter with Alejandro Ruizesesparza from the Cancel ShotSpotter Coalition and Jonathan Manes, an attorney in the MacArthur Justice Center’s Illinois Office. Our discussion focuses on why activists and communities are rising up to confront the Chicago Police Department on their use of ShotSpotter.

This report was researched and written by dozens of community members collaborating through the Stop LAPD Spying Coalitionʼs Land and Policing Workgroup. Over the past decade, the Stop LAPD Spying Coalition has been building community power to abolish LAPD surveillance. This report grew out of that organizing and examines the relationship of data-driven policing to real estate development, displacement, and gentrification.

While more people are beginning to understand the role of data in policing, less attention is paid to data-driven policingʼs relationship to land. This report studies that relationship with a focus on the process that has always bound policing and capitalism together: colonization. The report also examines the evolution of datadriven policing, including through LAPDʼs new Data-Informed CommunityFocused Policing, which combines data-mining and surveillance with the reformist notions of “community policing” and “police accountability.” This report is intended to frame an organizing agenda against this new program and beyond.

This essay is part of the ongoing “AI Lexicon” project, a call for contributions to generate alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about artificial intelligence (AI).

This report delves into the rhetoric of “smart borders” to explore their ties to a broad regime of border policing and exclusion that greatly harms migrants and refugees who either seek or already make their home in the United States. Investment in an approach centered on border and immigrant policing, it argues, is incompatible with the realization of a just and humane world.

Police departments in some of the largest U.S. cities have been experimenting with predictive policing as a way to forecast criminal activity. Predictive policing uses computer systems to analyze large sets of data, including historical crime data, to help decide where to deploy police or to identify individuals who are purportedly more likely to commit or be a victim of a crime.

Proponents argue that predictive policing can help predict crimes more accurately and effectively than traditional police methods. However, critics have raised concerns about transparency and accountability. Additionally, while big data companies claim that their technologies can help remove bias from police decision-making, algorithms relying on historical data risk reproducing those very biases.

Predictive policing is just one of a number of ways police departments in the United States have incorporated big data methods into their work in the last two decades. Others include adopting surveillance technologies such as facial recognition and social media monitoring. These developments have not always been accompanied by adequate safeguards.

Over the last three and a half years, the City of Detroit has greatly expanded Project Green Light, an initiative of the Detroit Police Department (DPD), along with local businesses and other organizations, to use video surveillance and digital technology to fight crime. Since the first cameras went live in eight gas stations on January 1, 2016, the system has grown as of April 2020 to nearly 700 locations across the city.

Though it is billed by proponents as a “real-time crime-fighting” solution, others, including the DSA, see it as a mass-surveillance system that disproportionately singles out communities of color. In particular, critics cite flaws in the technology behind the project that are part of what sociologist Ruha Benjamin, in her study Race After Technology, terms the “New Jim Code.”

In this report, OIG details ShotSpotter’s functionality and descriptive statistics regarding law enforcement activity related to CPD’s response to ShotSpotter alerts. OIG does not issue recommendations associated with this descriptive data. OIG is issuing this analysis of the outcomes of ShotSpotter alerts to provide the public and City government officials—to the extent feasible given the quality of OEMC and CPD’s data—with clear and accurate information regarding CPD’s use of ShotSpotter technology.

OIG concluded from its analysis that CPD responses to ShotSpotter alerts rarely produce documented evidence of a gun-related crime, investigatory stop, or recovery of a firearm. Additionally, OIG identified evidence that the introduction of ShotSpotter technology in Chicago has changed the way some CPD members perceive and interact with individuals present in areas where ShotSpotter alerts are frequent.

Defund. Re-Envision. Transform. is a grassroots campaign anchored by Action St. Louis, CAPCR, Forward Through Ferguson, and ArchCity Defenders, which demands the defunding of the St. Louis Metropolitan Police Department (SLMPD), the re-envisioning public safety through reinvestment into community resources that actually keep our communities safe, and transformation of the St. Louis region.