AI incident #896 ·
Alleged Misuse of Facial Recognition Technology by Law Enforcement Reportedly Leading to Wrongful Arrests and Violations of Investigative Standards
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 (AI Incident Database)
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.
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.
Who was involved
- Alleged deployer
- Florence Kentucky Police Department Evansville Indiana Police Department Detroit Police Department Coral Springs Florida Police Department Bradenton Florida Police Department Austin Police Department
- Alleged developer
- Facial recognition system developers Developers of mugshot recognition software Clearview AI
- 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)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.3 Lack of capability or robustness
- 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...
- 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...
- 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...
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Linked by AIID editors or by its text-similarity model.
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