AI incident ·
NYPD Facial Recognition System Allegedly Produced Erroneous Match That Reportedly Resulted in Wrongful Detention of Trevis Williams
In brief
An AI system built by Facial recognition system developers and deployed by NYPD, New York Police Department and 2 others allegedly harmed Trevis Williams, Law enforcement and 3 others.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 1 report
What happened
The NYPD's facial recognition system allegedly misidentified Trevis Williams as a suspect in a Union Square indecent exposure case. Despite reportedly notable physical differences and exculpatory phone data, Williams was arrested, jailed for more than two days, and charged. The case was later dismissed.
Editor's notes
Timeline note: This incident ID takes 04/21/2025 as the incident ID date because that is when Trevis Williams was reportedly arrested and jailed following an alleged facial recognition misidentification. The original crime was reported on 02/10/2025, and Williams was allegedly taken into custody on 04/21/2025. The charges were purportedly dismissed in July 2025, and The New York Times subsequently reported on the case on 08/26/2025 (the date of ingestion into the database as well).
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 (1)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged developer
- Facial recognition system developers
- Alleged harmed party
- Trevis Williams Law enforcement Judicial integrity General public of New York General public
AI systems implicated
Facial recognition systemsAI-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.
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Other incidents involving NYPD
Source record: incident #1191 on the AI Incident Database · all 1 report