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Alleged Misuse of Facial Recognition Technology by Law Enforcement Reportedly Leading to Wrongful Arrests and Violations of Investigative Standards

1 news report Synced from source · record last edited 4 Sep 2026

In brief

An AI system built by Facial recognition system developers, Developers of mugshot recognition software and 1 other and deployed by Florence Kentucky Police Department, Evansville Indiana Police Department and 4 others allegedly harmed Wrongfully arrested individuals, Vulnerable communities and 9 others.

Risk domain
AI system safety, failures, and limitations Lack of capability or robustness
Occurred
Coverage
1 reportJan 2025

What happened

Law enforcement agencies across the U.S. have allegedly been misusing AI-powered facial recognition technology, leading to wrongful arrests and significant harm to at least eight individuals. Officers have reportedly been bypassing investigative standards, relying on uncorroborated AI matches to build cases, allegedly resulting in prolonged detentions, reputational damage, and personal trauma.

Editor's notes

Editor Notes: This collective incident ID, based on a Washington Post investigation, tracks alleged misuse of facial recognition technology by law enforcement across the U.S., similar to Incident 815: Police Use of Facial Recognition Software Causes Wrongful Arrests Without Defendant Knowledge. While that incident focuses on allegations of withholding information regarding arrests, this incident focuses on reports of law enforcement allegedly relying primarily on facial recognition technology without sufficient corroborative investigative procedures. Some reported incidents include: (1) December 2020: Facial recognition technology reportedly misidentified Christopher Gatlin in Missouri, resulting in his arrest and over 16 months in jail before charges were dropped in March 2024. (2) 2022: Maryland police allegedly misidentified Alonzo Sawyer for assault using facial recognition; his wife later provided evidence that reportedly cleared his name. (3) 2022: Detroit police arrested Robert Williams based on a reported facial recognition error; the city later settled a lawsuit in 2023 for $300,000 without admitting liability. (4) July 2024: Miami police reportedly relied on facial recognition to identify Jason Vernau for check fraud; he was jailed for three days before charges were dropped. (5) January 13, 2025: The Washington Post published its investigation, detailing at least eight wrongful arrests reportedly linked to the use of facial recognition technology and alleged failures to corroborate AI-generated matches. See the full report at The Washington Post for more details on specific cases, timelines, and deployers of this technology.

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.

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 (1)

Titles link to the original publisher; report text is not reproduced here.

  1. Arrested by AI: Police ignore standards after facial recognition matches
    washingtonpost.com · Douglas MacMillan, David Ovalle, Aaron Schaffer

Who was involved

Alleged harmed party
Wrongfully arrested individuals Vulnerable communities Robert Williams Quran Reid Porcha Woodruff People of color Nijeer Parks Jason Vernau Christopher Gatlin Black people Alonzo Sawyer

AI systems implicated

Statewide Network of Agency Photos (SNAP)St. Louis mugshot recognition technologyFlorida state facial recognition systemFacial recognition systemsClearview AIAI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
Human
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 )

  • 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...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

Linked by editors or by text similarity in the source dataset.

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #896 on the AI Incident Database · all 1 report