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School shooting survivor sues AI gun detection firm after system failed to spot weapon (arstechnica.com)
submitted 5 months ago by to c/technology@lemmy.world
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The injured teenage survivor of a January 2025 shooting at a Nashville, Tennessee high school recently sued the manufacturer of an “AI gun detection” system that failed to detect the handgun that left two dead, including the shooter.

According to the lawsuit, which was filed in Davidson County court last month, the security company Omnilert either knew or should have known that there were “significant operational limitations in its gun detection system that could result in detection failures during actual emergencies, including limitations based on camera placement, proximity of the weapon to camera sensors, camera angle, lighting, and weapon visibility.”

Omnilert cofounder Ara Bagdasarian declined Ars’ invitation to answer questions about the lawsuit. System Integrations, the other defendant in the case, which resold the Omnilert system, also did not respond to Ars’ request for comment.

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[–] 4 points 5 months ago (4 children)

Using a LLM for detecting a specific object on an image is possible but stupid: if your object is always the same (like in this case) it's several orders of magnitude cheaper to train once on that specific object then use the computer vision model running directly on the local server that's recording the video.

Otherwise:

  1. the api costs would be colossal, 0.001$ per each image, at 30 fps it's $100 per hour, nobody would pay that
  2. The detection latency would be several seconds vs almost instant
  3. Without internet connection the system wouldn't work

Use cases for LLM-based image recognition is if the object changes at every request or it's ultra specific with brands and colors

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  • [–] -1 points 5 months ago (3 children)

    if your object is always the same (like in this case)

    It isn't the same though. A large gauge shotgun and a small gauge pistol are pretty different looking. Compare those to a .22 rifle with a scope, and those to a decked out ar15. That's a lot of different always the sames. What if it's a revolver? Or has a folded stock? Or a sawed off stock? Will it recognize a derringer or a mac10 with a large capacity mag as guns?

    We can because they make us dead. We have valid reason to fear them which is a great motivator for most species to learn to recognize the danger. You'd still recognize a ring gun as a gun, without getting specifically trained to do so a machine will identify it as jewelry.

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  • [–] 5 points 5 months ago (1 child)

    so, train the computer vision model for a gun and train again for a shotgun. Run the two detection models at the same time.

    Your approach is the typical "but if you really want you can use an atomic bomb to kill mosquitoes" - yes, you could do that, but nobody is paying $1 mil/year in inference costs (+some expensively licensed software to wrap around that) when it can be done locally with a $300 GPU (+ some expensively licensed software to wrap around that)

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  • [–] 3 points 5 months ago

    A large gauge shotgun and a small gauge pistol are pretty different looking. Compare those to a .22 rifle with a scope, and those to a decked out ar15. That's a lot of different always the sames. What if it's a revolver? Or has a folded stock? Or a sawed off stock? Will it recognize a derringer or a mac10 with a large capacity mag as guns?

    You seem to think that computer vision models can only be trained on a single thing. You simply train your modem on as many object types as you want it to be aware of. That's it.

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