In the late 2010s, a group of sheriffs out of Pasco County, Florida, believed they could predict crime. The Sheriff’s Department there had piloted a program called “Intelligence-Led Policing” and the program would allegedly analyze disparate points of data to identify would-be criminals.

But in reality, the program didn’t so much predict crime, as it did make criminals out of everyday people, including children. 

High schoolers’ grades were fed into the Florida program, along with their attendance records and their history with “office discipline.” And after the “Intelligence-Led Policing” service analyzed the data, it instructed law enforcement officers on who they should pay visit to, who they should check in on, and who they should pester.

As reported by The Tampa Bay Times in 2020:

“They swarm homes in the middle of the night, waking families and embarrassing people in front of their neighbors. They write tickets for missing mailbox numbers and overgrown grass, saddling residents with court dates and fines. They come again and again, making arrests for any reason they can.One former deputy described the directive like this: ‘Make their lives miserable until they move or sue.’”

Predictive policing can sound like science fiction, but it is neither scientific nor is it confined to fiction.

Police and sheriff’s departments across the US have used these systems to plug broad varieties of data into algorithmic models to try and predict not just who may be a criminal, but where crime may take place. Historical crime data, traffic information, and even weather patterns are sometimes offered up to tech platforms to suggest where, when, and how forcefully police units should be deployed.

And when the police go to those areas, they often find and document minor infractions that, when reported, reinforce the algorithmic analysis that an area is crime-ridden, even if those crimes are, as the Tampa Bay Times investigation found, a teenager smoking a cigarette, or stray trash bags outside a home.

Today, on the Lock and Code podcast with host David Ruiz, we speak with Emily Galvin-Almanza, cofounder of Partners for Justice and author of the upcoming book “The Price of Mercy,” about predictive policing, its impact on communities, and the dangerous outcomes that might arise when police offload their decision-making to data.

“ I am worried about anything that a data broker can sell, they can sell to a police department, who can then feed that into an algorithmic or AI predictive policing system, who can then use that system—based on the purchases of people in ‘Neighborhood A’—to decide whether to hyper-police ‘Neighborhood A.’”

Tune in today.

You can also find us on Apple Podcasts, Spotify, and whatever preferred podcast platform you use.

For all our cybersecurity coverage, visit Malwarebytes Labs at malwarebytes.com/blog.

Show notes and credits:

Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 4.0 License

http://creativecommons.org/licenses/by/4.0/

Outro Music: “Good God” by Wowa (unminus.com)

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