AI incident ·
Alleged False Positive by Omnilert AI Gun Detection System Prompts Police Search at Baltimore County High School
In brief
An AI system built by Omnilert and deployed by Baltimore County Public Schools allegedly harmed Taki Allen, Students and 4 others.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 3 reports
What happened
A purportedly AI-powered gun detection system at Kenwood High School in Baltimore County, Maryland, reportedly misidentified a student's empty Doritos bag as a firearm. Armed police reportedly detained and handcuffed the student before determining there was no weapon. The AI system, operated by Omnilert, issued the false alert, leading to public outcry and an official review of school security protocols. Omnilert reportedly expressed regret for the incident.
Editor's notes
See also Incident 1267: Omnilert AI Reportedly Triggered False Gun Alert at Parkville High, Prompting Student Relocation.
Laws that address this harm
Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
- Texas Responsible AI Governance Act (TRAIGA)
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (3)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Baltimore County Public Schools
- Alleged developer
- Omnilert
- Alleged harmed party
- Taki Allen Students Minors Kenwood High School students High school students Educational communities
AI systems implicated
Weapons detection systemOmnilertAI-enabled video surveillance system for gun detectionAI-enabled decision support systems
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.3 Lack of capability or robustness
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 7.3.
- Reliability issues
"Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...
- Type 2: Bigger than expected
Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.
- Type 3: Worse than expected
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
- Ethics and Morality Issues
LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.
- Safe learning
"AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."
- Malign belief distributions
"Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...
- Meta-cognition
"Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...
- Technical and operational risks
"To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...
Related incidents
Linked by editors or by text similarity in the source dataset.
- Omnilert AI Reportedly Triggered False Gun Alert at Parkville High, Prompting Student Relocation
- Detroit Police Allegedly Wrongfully Arrested Black Man Due to Purportedly Faulty Facial Recognition Technology
- Security Robot Rolls Over Child in Mall
- Skating Rink’s Facial Recognition Cameras Misidentified Black Teenager as Banned Troublemaker
Incidents in the same risk subdomain
- Purported AI Name-Reading System Reportedly Skipped and Misannounced Graduates at Arizona's Glendale Community College Commencement
- PocketOS Production Database Was Reportedly Deleted by Cursor AI Agent Running Claude Opus 4.6
- Baidu Apollo Go Robotaxis Stopped in Traffic During Reported System Failure in Wuhan, Stranding Some Passengers
- Purportedly AI-Enabled Targeting System Was Reportedly Implicated in Deadly U.S. Strike on Iranian Primary School
- Claude Code Agent Reportedly Deleted DataTalks.Club Production Infrastructure, Database, and Snapshots via Terraform
- Purportedly AI-Generated Sepsis Alert Reportedly Prompted Potentially Inappropriate IV Fluid Administration for a Dialysis Patient, Averted by Clinician Intervention
Other incidents involving Baltimore County Public Schools
Source record: incident #1250 on the AI Incident Database · all 3 reports