WTF: How AI Technology Can Send You to Prison, Even If There Are No Eyewitnesses

The last thing the justice system needed was another way to put innocent Black people in jail.

ShotSpotter, an AI-powered surveillance system that’s meant to accurately detect the sound and location of gunshots, was the prosecution’s biggest piece of evidence when a 25-year-old man, Safarain Herring, was shot in Chicago last May.

According to an investigation by the Associated Press, Michael Williams was accused of getting caught on the surveillance system at the same time that Herring was shot. Police reportedly accused Williams of shooting the young man and dropping him off at the local hospital.

“I kept trying to figure out, how can they get away with using the technology like that against me?” Williams said to reporters,“That’s not fair.” He was arrested last August.

According to Motherboard Tech by Vice, the prosecution withdrew the charges because Williams’ public defender found that ShotSpotter analysts made changes in classifying the sound that hidden microphones in the surveillance system picked up that night. Williams was in jail for nearly a year before prosecutors admitted to not having sufficient evidence to try him.

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From Vice:

The company’s algorithms initially classified the sound as a firework. That weekend had seen widespread protests in Chicago in response to George Floyd’s murder, and some of those protesting lit fireworks.

But after the 11:46 p.m. alert came in, a ShotSpotter analyst manually overrode the algorithms and “reclassified” the sound as a gunshot. Then, months later and after “post-processing,” another ShotSpotter analyst changed the alert’s coordinates to a location on South Stony Island Drive near where Williams’ car was seen on camera.

However, according to AP’s investigation, human error is not the only thing that can affect ShotSpotters efficacy and reliability. The investigation found that the surveillance system can miss live gunfire right under its microphones. It has also misidentified gunshots and hasn’t been shown to reduce gun violence or improve community safety.

Investigators say that the surveillance system has been placed in about 110 U.S. cities at the request of local officials in places that are “more likely to have gun violence,” which has turned out to be predominantly Black and Latino neighborhoods. ShotSpotter won’t release its algorithm so that defense attorneys can properly assess the technology, even though it’s already been admitted in about 200 court cases.

The surveillance technology has also appeared in many high-profile cases. In 2016, ShotSpotter was the only piece of evidence against Silvon Simmons, a Black man who was accused of firing back at police when he was a victim of mistaken identity. And in another case it was a ShotSpotter alert that sent police to 13-year-old Adam Toledo’s neighborhood before they shot and killed him back in March.

From Vice:

“These tools are sending more police into Black and Latinx neighborhoods,” Alyx Goodwin, a Chicago organizer with the Action Center on Race and the Economy, one of the groups leading the campaign, told Motherboard. “Every ShotSpotter alert is putting Black and Latinx people at risk of interactions with police. That’s what happened to Adam Toledo.”’

Motherboard recently obtained data demonstrating the stark racial disparity in how Chicago has deployed ShotSpotter. The sensors have been placed almost exclusively in predominantly Black and brown communities, while the white enclaves in the north and northwest of the city have no sensors at all, despite Chicago police data that shows gun crime is spread throughout the city.

Last month, Chicago law enforcement was called to end their contract with ShotSpotter after it was found that officers have requested analysts to change classifications of certain noises to gunshots. Shotspotter CEO Ralph Clark has stood by the accuracy of his technology, “The point is anything that ultimately gets produced as a gunshot has to have eyes and ears on it,” said Clark, according to Vice. “Human eyes and ears, OK?”

Well, Ralph, it’s not very helpful when those eyes and ears are attached to the shoot-first-ask-questions-later type of humans.