AI Gun Detection System Mistakes Doritos Bag for Weapon at Baltimore High School, Sparking Police Response

Reviewed byNidhi Govil

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An AI-powered gun detection system at a Baltimore high school falsely identified a student's Doritos bag as a firearm, leading to a police confrontation. This incident raises significant concerns about AI reliability in security applications and the potential for false alarms to traumatize students and prompt unnecessary law enforcement actions.

AI System's Dangerous Blunder in Baltimore

An AI-powered gun detection system at Kenwood High School in Baltimore mistakenly identified a student's Doritos bag as a firearm, prompting an alarming police response. This incident highlights critical concerns regarding the accuracy of artificial intelligence in security roles and the traumatic potential of false alarms in schools

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Student's Frightening Encounter

Taki Allen, the student, was confronted by armed police officers after the Omnilert-provided AI system flagged his crumpled snack bag. Body camera footage shows officers drawing weapons, detaining, and handcuffing Allen before confirming no weapon was present

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. Allen described the terrifying experience, fearing for his life during the ordeal

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. School officials quickly cleared the alert; Baltimore County Police confirmed responding to a report of an armed individual, finding none

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Source: CBS News

Source: CBS News

Calls for AI Security Review

This misidentification intensified a demand for a review of AI weapon detection systems in schools. Omnilert faces scrutiny despite reliability claims

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. Baltimore County Councilman Izzy Pakota urged BCPS to reassess AI procedures

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. The event echoes past issues, like US firm Evolv Technology being barred from unsubstantiated claims about its widely used AI scanners

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. Councilman Julian Jones questioned, "How did it come to be that we had police officers with guns drawn approaching a kid because of a bag of Doritos?"

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As AI integrates further into public safety, this episode underscores its limitations and the urgent need for stringent testing, careful deployment, and robust oversight to prevent dangerous misidentifications.

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