AI incident #816 ·
Cross-Jurisdictional Facial Recognition Misidentification by NYPD Leads to Wrongful Arrest and Four-Year Jail Time in New Jersey
What happened
In 2019, facial recognition technology misidentified Francisco Arteaga as a suspect in an armed robbery in New Jersey. The incident led to nearly four years of pretrial incarceration. Despite having an alibi, Arteaga was charged based on the flawed identification. The legal battle that followed resulted in a court ruling requiring police to reveal details about the algorithms used in facial recognition. The process exposed significant gaps in transparency and accountability.
Editor's notes (AI Incident Database)
See Incident 815 for a broader overview of these specific kinds of harms. Reconstructing the timeline of events: (1) November 29, 2019: An armed robbery occurs at the Buenavista Multiservices store in West New York, New Jersey. Police submit surveillance footage for facial recognition analysis. (2) December 2019: The West New York Police Department sends surveillance footage to the NYPD's Real Time Crime Center, which identifies Francisco Arteaga as a possible match using facial recognition technology. (3) 2019-2022: Arteaga spends nearly four years in pretrial detention while fighting the charges, despite having an alibi. (4) May 13, 2022: A trial judge denies Arteaga’s motion for discovery on details of the facial recognition technology used in his case. (5) June 7, 2023: A New Jersey appellate court rules that Arteaga is entitled to information on the facial recognition technology used in his case, including the algorithm, error rates, and other relevant details.
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
- West New York PD Real Time Crime Center NYPD Law enforcement Facial recognition system deployers
- Alleged developer
- Facial recognition system developers Clearview AI
- Alleged harmed party
- Francisco Arteaga
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...
- 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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