AI Misidentifies Chip Bag as Gun at Kenwood High School
Nov 19, 2025
Table of Contents
Early Warning Signs: How AI Misread a Harmless Moment
How the AI System Works
The False-Positive Breakdown
Emotional Impact on Students
Understanding AI Bias and Error Rates
Wider Implications for AI Surveillance in Schools
Similar Incidents and Key Lessons
Best Practices for AI Oversight
Conclusion: Balancing Safety and Humanity
KEY TAKEAWAYS
An AI system at Kenwood High School misidentified a Doritos bag as a weapon, prompting an armed police response.
The event exposes the limits of pattern-based AI, the dangers of automated escalation, and the absence of contextual reasoning.
Students experienced emotional trauma, highlighting the need for proper oversight and human verification.
Experts emphasise ethical review, strict auditing, and responsible use of AI in school environments.
In October 2025, a normal afternoon at Kenwood High School in Maryland became scary. An AI security system wrongly saw a crumpled Doritos bag as a gun. The system’s automatic alert triggered a rapid and heavily armed police response involving multiple patrol cars and police officers with weapons drawn, leaving 16-year-old student Taki Allen face-down in handcuffs. The incident ended without injury. But it showed serious problems in how schools use artificial intelligence for campus security.
The event showed many problems. AI systems can misread visual signs. Automated alerts can quickly call the police. Normal student actions can look like threats. This type of misclassification is a real AI mistake. It happens when AI fails to understand context. It showcased an AI context failure similar to what researchers call AI hallucination, a pattern explored in the study “Vision-Language Models Hallucinate Objects”.
How the AI System Works
Kenwood High School uses an AI safety platform developed by Omnilert, which was introduced to the school district in 2023. The system continuously scans school security camera feeds, looking for shapes and contours that resemble weapons. When something matches its internal pattern library, the system automatically sends real-time alerts to school administrators and local police. The intended goal is simple: stop potential violence before it begins, using a predictive algorithm.
But the system operates purely on pattern comparison. It does not understand context, intent, or the difference between a metallic reflection on a chip bag and an actual firearm. Investigators think the AI confused the angles and shiny parts of the Doritos bag for a gun shape. The AI’s misclassification of the Doritos bag as a firearm illustrates how machine learning can find patterns without understanding their meaning. It also shows limits in training data. These problems affect data security, recognition stability, and detection reliability in AI systems that use neural networks. (Source: People Magazine)
The False-Positive Breakdown
On October 20, after football practice, Allen sat outside the school eating chips. The AI system misinterpreted his posture and the reflective snack bag. Within moments, police descended on Kenwood High School—eight patrol cars responding to what the system believed was an imminent threat. Officers confronted Allen at gunpoint, ordered him to the ground, and handcuffed him before realising the “weapon” was a simple empty chip bag.
A key sequence emerged:
AI detected a perceived threat.
Operators attempted to retract the alert using AI response moderation protocols.
Police arrived before the retraction reached their radios.
Omnilert later asserted that the system “functioned properly” by escalating a potential threat. Critics argued that this logic dangerously normalises false positives, especially in an environment where a school resource officer or police officers act on automated alerts with high error rates. (Source: Fox News)
Also Read: The Rise of Deepfake: How Grok AI Fueled the Scandal
Emotional Impact on Students
For Allen, the experience was deeply traumatic. He later shared that he feared he might be shot during the confrontation. His family demanded accountability from both the school and Omnilert, insisting that reliance on flawed technology nearly cost him his life.
Kenwood High School administrators apologised and arranged counselling for students. Superintendent Myriam Rogers is committed to reassessing all AI safety tools used across the district. Yet for many students, trust had already eroded — raising fears that the very systems meant to protect them could instead cause harm.
This emotional fallout demonstrates why human verification must be central to school-based AI governance, especially when real-world legal cases have shown similar failures in other automated systems. (Source: WJBC)
Understanding AI Bias and Error Rates
AI surveillance systems promise to increase school safety, but research shows they produce a significant number of false positives. A 2024 EdTech Research Network report noted that even a ratio of one false positive per 4,000 alerts can be dangerous when police respond with lethal-force protocols.
AI bias compounds the issue. Recognition systems struggle across varying lighting, backgrounds, and skin tones. Experts like Dr. Joy Buolamwini warn that AI “sees patterns, not people”, leading to misinterpretations of normal student behaviour as potential threats. These issues stem from algorithmic bias, entity recognition errors, conversational AI misreads, and limitations within foundation models used for object detection.
Wider Implications for AI Surveillance in Schools
The Kenwood High School incident reflects growing national concerns. Over half of U.S. schools now use AI surveillance tools to monitor hallways, identify objects, and flag “risky” behaviour in digital communications. These tools often use technologies similar to GenAI, AI assistants, or natural language processing engines that attempt to classify student messages.
Critics say these systems can invade student privacy. They can cause stress and misread harmless actions. They can also have insider threats or be hacked. Proper security checks and access controls are needed.
Investigations have found cases where AI flagged benign messages as signs of violence or self-harm, reinforcing fears that these systems may generate more false alarms than meaningful protections.
Similar Incidents and Key Lessons
The Kenwood case recalls the infamous 2015 Ahmed Mohamed clock incident, when a Texas high school student was arrested after bringing a homemade clock mistaken for a bomb. Both events illustrate how fear and overreliance on technology—or assumptions—can lead to traumatic outcomes for young people. As AI tools become more pervasive, these mistakes risk becoming viral examples of misplaced technological trust.
These events reinforce a fundamental ethical message: context matters, and human judgement must guide safety decisions, particularly in school settings. This is at the core of modern AI governance principles promoted at global events such as the AI Safety Summit.
Best Practices for AI Oversight
To prevent future false alarms, experts recommend a hybrid safety model combining automation with human verification:
Human-in-the-loop protocols – Alerts should never trigger a law enforcement response without live, manual verification by trained security staff.
Regular system audits – Schools must evaluate algorithm performance rates, track false positives, and ensure models are retrained on diverse datasets.
Transparent communication – Students and parents should understand how surveillance systems operate and how data is used.
Bias screening and ethics training – AI operators require ongoing education on bias, context, and crisis management.
Post-event support – Offer counselling and debriefings following false alarms to rebuild trust and emotional safety.
These recommendations echo guidance from the U.S. Department of Education’s Office of Educational Technology, which urges schools to “maintain human accountability in every AI-driven decision impacting student safety or privacy.”
Conclusion: Balancing Safety and Humanity
The Kenwood High School “chip bag incident” is both surreal and sobering. It demonstrates how easily automated systems can escalate harmless situations into dangerous ones when context is absent and human oversight is insufficient. As schools use more AI for safety, they must focus on being open and responsible. They should also be caring. Technology must not harm student dignity or safety.
Also Read: Deepfake Arrest: IIIT Raipur Scandal Sparks AI Law Debate
FAQs
1. What mistakes has AI made?
AI has made a wide range of mistakes, including misidentifying everyday objects (like mistaking a chip bag for a gun), falsely flagging harmless messages as threats, generating incorrect or fabricated information, misdiagnosing medical images, and making biased decisions based on flawed training data. These errors usually come from how AI models interpret patterns without real-world context or understanding.
2. Does AI make a lot of mistakes?
Yes. Even advanced systems make frequent mistakes, especially in real-world conditions. Many of these errors stem from context handling failures, limited training data, confusing inputs, poor lighting, and situations requiring human judgment or common sense.
3. What are common mistakes that AI makes that humans never would?
AI makes mistakes because it doesn’t understand meaning, intent, or context. It only detects patterns in data. Errors happen due to:
limited or biased training data
poor generalization to new situations
ambiguity in real-world inputs
lack of common-sense reasoning
overconfidence in its predictions
algorithmic bias or gaps in the model
Even the most advanced AI is not truly intelligent—its “knowledge” is based on statistical patterns, not comprehension.
4. Why does AI make mistakes during tasks even when it’s supposed to be highly advanced?
AI makes mistakes because it doesn’t understand meaning or real-world context. It only processes patterns in data. Errors occur due to:
context handling failures
limited or biased training data
poor generalization
ambiguous inputs
lack of common-sense reasoning
algorithmic bias
Even the most advanced AI systems operate statistically, not conceptually, which makes them vulnerable to misinterpretation.
