Confidential document reveals key human role in gunshot tech
CHICAGO — In more than 140 cities across the United States, ShotSpotter’s artificial intelligence algorithm and intricate network of microphones evaluate hundreds of thousands of sounds a year to determine if they are gunfire, generating data now being used in criminal cases nationwide.
But a confidential ShotSpotter document obtained by The Associated Press outlines something the company doesn’t always tout about its “precision policing system” — that human employees can quickly overrule and reverse the algorithm’s determinations, and are given broad discretion to decide if a sound is a gunshot, fireworks, thunder or something else.
Such reversals happen 10 percent of the time by a 2021 company account, which experts say could bring subjectivity into increasingly consequential decisions and conflict with one of the reasons AI is used in law-enforcement tools in the first place — to lessen the role of all-too-fallible humans.
“I’ve listened to a lot of gunshot recordings — and it is not easy to do,” said Robert Maher, a leading national authority on gunshot detection at Montana State University who reviewed the ShotSpotter document. “Sometimes it is obviously a gunshot. Sometimes it is just a ping, ping, ping. … and you can convince yourself it is a gunshot.”
Marked “WARNING: CONFIDENTIAL,” the 19-page operations document spells out how employees in ShotSpotter’s review centers should listen to recordings and assess the algorithm’s finding of likely gunfire based upon a series of factors that may require judgment calls, including whether the sound has the cadence of gunfire, whether the audio pattern looks like “a sideways Christmas tree” and if there is “100 percent certainty of gunfire in reviewer’s mind.”
ShotSpotter said in a statement to the AP that the human role is a positive check on the algorithm and the “plain-language” document reflects the high standards of accuracy its reviewers must meet.
“Our data, based on the review of millions
of incidents, proves that human review adds value, accuracy and consistency to a review process that our customers—and many gunshot victims—depend on,” said Tom Chittum, the company’s vice president of analytics and forensic services.
Chittum added that the company’s expert witnesses have testified in 250 court cases in 22 states, and that its “97 percent aggregate accuracy rate for real-time detections across all customers” has been verified by an analytics firm the company commissioned.
Another part of the document underscores ShotSpotter’s longstanding emphasis on speed and decisiveness, and its commitment to classify sounds in less than a minute and alert local police and 911 dispatchers so they can send officers to the scene.
Titled “Adopting a New York State of Mind,” it refers to New York Police Department’s request of ShotSpotter to avoid posting alerts of sounds as “probable gunfire” — only definitive classifications as gunfire or non-gunfire.
“End result: It trains the reviewer to be decisive and accurate in their classification and attempts to remove a doubtful publication,” the document reads.
Experts say such guidance under tight time pressure could encourage ShotSpotter reviewers to err in favor of categorizing a sound as a gunshot, even if some evidence for it falls short, potentially boosting the numbers of false positives.
“You’re not giving your humans much time,” said Geoffrey Morrison, a voicerecognition scientist based in Britain who specializes in forensics processes. “And when humans are under great pressure, the possibility of mistakes is higher.”
ShotSpotter says it published 291,726 gunfire alerts to clients in 2021. That same year, in comments to AP appended to a previous story, ShotSpotter said more than 90 percent of the time its human reviewers agreed with the machine classification but the company invested in its team of reviewers “for the 10 percent of the time where they disagree with the machine.” ShotSpotter did not respond to questions on whether that ratio still holds true.