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AI incident #1167 ·

Alleged Gaggle Surveillance Alert Reportedly Leads to Arrest and Detention of 13-Year-Old Student in Fairview, Tennessee

Open on the AI Incident Database 1 news report Synced from the AIID API · record last edited 4 Sep 2026

What happened

Sometime in August 2023, a 13-year-old student at Fairview Middle School in Tennessee was reportedly arrested, strip-searched, and detained overnight after Gaggle's purported AI-powered surveillance system flagged a private online message as a threat. The student's comment, reportedly a joke in context, was sent to law enforcement under a state zero-tolerance law. The incident reportedly resulted in house arrest, alternative school placement, and a lawsuit alleging wrongful arrest.

Editor's notes (AI Incident Database)

Timeline note: The incident ID date of 08/15/2023 is an approximation based on available reporting about the August 2023 arrest of a 13-year-old student in Fairview, Tennessee, following a Gaggle surveillance alert. The exact day of the arrest was not specified in the sourcing. Reporting on this incident appears to have begun 08/08/2025. The incident ID was made 08/10/2025.

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 (1)

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

  1. Students have been called to the office — and even arrested — for AI surveillance false alarms
    apnews.com · Sharon Lurye · AIID #6094

Who was involved

Alleged developer
Gaggle
On AIID: Gaggle
Alleged harmed party
Unnamed 13-year-old student from Fairview, Tennessee Students Privacy Minors Educational communities Biometric data subjects
On AIID: Unnamed 13-year-old student from Fairview, Tennessee, Students, Privacy, Minors, Educational communities, Biometric data subjects

AI systems implicated

GaggleAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

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...

    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)

Linked by AIID editors or by its text-similarity model.

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