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AI incident #349 ·

Evolv AI Weapons Detection System Allegedly Misrepresents Accuracy, Leading to School Security Gaps

Open on the AI Incident Database 3 news reports Synced from the AIID API · record last edited 3 Sep 2026

What happened

Evolv's AI-powered weapons scanners were advertised as superior to metal detectors but reportedly failed to detect actual weapons while generating excessive false positives. The FTC alleged that misleading claims about the system's accuracy and speed contributed to schools relying on unreliable detection, with at least one incident in October 2022 involving a missed knife that resulted in a student being stabbed.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (3)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. ‘The Least Safe Day’: Rollout of Gun-Detecting AI Scanners in Schools Has Been a ‘Cluster,’ Emails Show
    vice.com · Aaron Gordon, Janus Rose · AIID #2095
  2. Gun Detection AI The Latest Tech To Make Schools Less Safe
    techdirt.com · Tim Cushing · AIID #2065
  3. AI weapon-detection firm ‘deceptively’ advertised to schools, venues, says FTC
    statescoop.com · Keely Quinlan · AIID #5464

Who was involved

Alleged developer
Evolv Technology
On AIID: Evolv Technology
Alleged harmed party
teachers at Charlotte Mecklenburg Schools students at Charlotte Mecklenburg Schools Students security officers at Charlotte Mecklenburg Schools School administrators at Charlotte Mecklenburg Schools Parents of students at Charlotte Mecklenburg Schools Epistemic integrity Educational communities
On AIID: teachers at Charlotte Mecklenburg Schools, students at Charlotte Mecklenburg Schools, Students, security officers at Charlotte Mecklenburg Schools, School administrators at Charlotte Mecklenburg Schools, Parents of students at Charlotte Mecklenburg Schools, Epistemic integrity, Educational communities

AI systems implicated

Weapons detection systemEvolv Express weapons detection systemAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
AI tangible harm issue
Sectors
education, arts, entertainment and recreation
Countries
US

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...

    International AI Safety Report 2025 (Bengio2025)

  • 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.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • 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.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • 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.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • 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)."

    AGI Safety Literature Review (Everitt2018 )

  • 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...

    AGI Safety Literature Review (Everitt2018 )

  • 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...

    AGI Safety Literature Review (Everitt2018 )

  • Misaligned consequentialist reasoning

    "As we think about even more intelligent and advanced AI assistants, perhaps outperforming humans on many cognitive tasks, the question of how humans can successfully control such an assistant looms large. To achieve the...

    The Ethics of Advanced AI Assistants (Gabriel2024)

Incidents in the same risk subdomain

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