AI incident #1476 ·
Maryland Police Allegedly Relied on Facial Recognition Lead in Wrongful Arrest and Detention of Kimberlee Williams
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 (AI Incident Database)
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.
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
- 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...
- 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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