Washington, DC, is the home base of the most powerful government on earth. It’s also home to 690,000 people—and 29 obscure algorithms that shape their lives. City agencies use automation to screen housing applicants, predict criminal recidivism, identify food assistance fraud, determine if a high schooler is likely to drop out, inform sentencing decisions for young people, and many other things.
That snapshot of semiautomated urban life comes from a new report from the Electronic Privacy Information Center (EPIC). The nonprofit spent 14 months investigating the city’s use of algorithms and found they were used across 20 agencies, with more than a third deployed in policing or criminal justice. For many systems, city agencies would not provide full details of how their technology worked or was used. The project team concluded that the city is likely using still more algorithms that they were not able to uncover.
The findings are notable beyond DC because they add to the evidence that many cities have quietly put bureaucratic algorithms to work across their departments, where they can contribute to decisions that affect citizens’ lives.
Government agencies often turn to automation in hopes of adding efficiency or objectivity to bureaucratic processes, but it’s often difficult for citizens to know they are at work, and some systems have been found to discriminate and lead to decisions that ruin human lives. In Michigan, an unemployment-fraud detection algorithm with a 93 percent error rate caused 40,000 false fraud allegations. A 2020 analysis by Stanford University and New York University found that nearly half of federal agencies are using some form of automated decisionmaking systems.
EPIC dug deep into one city’s use of algorithms to give a sense of the many ways they can influence citizens’ lives and encourage people in other places to undertake similar exercises. Ben Winters, who leads the nonprofit’s work on AI and human rights, says Washington was chosen in part because roughly half the city’s residents identify as Black.
“More often than not, automated decisionmaking systems have disproportionate impacts on Black communities,” Winters says. The project found evidence that automated traffic-enforcement cameras are disproportionately placed in neighborhoods with more Black residents.
Cities with significant Black populations have recently played a central role in campaigns against municipal algorithms, particularly in policing. Detroit became an epicenter of debates about face recognition following the false arrests of Robert Williams and Michael Oliver in 2019 after algorithms misidentified them. In 2015, the deployment of face recognition in Baltimore after the death of Freddie Gray in police custody led to some of the first congressional investigations of law enforcement use of the technology.
EPIC hunted algorithms by looking for public disclosures by city agencies and also filed public records requests, requesting contracts, data sharing agreements, privacy impact assessments and other information. Six out of 12 city agencies responded, sharing documents such as a $295,000 contract with Pondera Systems, owned by Thomson Reuters, which makes fraud detection software called FraudCaster used to screen food-assistance applicants. Earlier this year, California officials found that more than half of 1.1 million claims by state residents that Pondera’s software flagged as suspicious were in fact legitimate.