AI incident ·
Maryland Police Allegedly Relied on Facial Recognition Lead in Wrongful Arrest and Detention of Kimberlee Williams
In brief
An AI system built by Facial recognition system developers and deployed by Prince George's County Police Department, Montgomery County Police Department and 3 others allegedly harmed Wrongfully arrested individuals, Privacy and 4 others.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 1 report
What happened
Kimberlee Williams was reportedly arrested in Oklahoma on Maryland warrants after police allegedly relied on a facial recognition lead that incorrectly identified her as a suspect in bank fraud cases. The ACLU said Williams had never been to Maryland, police failed to investigate alibi evidence placing her in Oklahoma, and she spent about six months in jail before the charges were dropped.
Editor's notes
Timeline notes: The incident date of 06/23/2021 marks the reported date of Kimberlee Williams's arrest, which was based on warrants from December 2019 and January 2020. The incident ID is based off of reporting from the ACLU's article of 04/14/2026. The incident ID was created 05/03/2026.
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 deployer
- Prince George's County Police Department Montgomery County Police Department Law enforcement Facial recognition system deployers Anne Arundel County Police Department
- Alleged developer
- Facial recognition system developers
- Alleged harmed party
- Wrongfully arrested individuals Privacy People misidentified by facial recognition systems Kimberlee Williams Epistemic integrity Biometric data subjects
AI systems implicated
Facial recognition systemsCrimedexAI-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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Source record: incident #1476 on the AI Incident Database · all 1 report