Descripción: NYC implemented an AI enabled weapons scanner for a month-long pilot with limited success. Despite not finding any weapons during the September 2024 testing phase, there were 118 false positives in which a person was searched under suspicion of carrying a weapon with no actual gun detections.
Editor Notes: The 118 false positives can be considered a privacy invasion, and according to some cited legal advocacy groups, a violation of due process. Reconstructing the timeline of events: (1) March 28, 2024: Mayor Eric Adams announces plans to deploy Evolv's AI-powered weapons scanners in selected NYC subway stations. (2) Summer 2024: Pilot program begins, deploying AI scanners across 20 subway stations. (3) September 2024: NYPD completes a 30-day testing period with the scanners, performing 2,749 scans and recording 118 false positives and no firearms detections. (4) October 23, 2024: NYPD releases a brief statement summarizing the pilot results, which is marked at the incident date for our purposes, even though each false positive (as well as the potential for firearms to have slipped past detection) may be considered discrete incidents in and of themselves.
Entidades
Ver todas las entidadesAlleged: Evolv Technology developed an AI system deployed by New York City Government, which harmed New York City subway riders.
Estadísticas de incidentes
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
7.3. Lack of capability or robustness
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- AI system safety, failures, and limitations
Entity
Which, if any, entity is presented as the main cause of the risk
AI
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Unintentional
Informes del Incidente
Cronología de Informes
NUEVA YORK (AP) — Un programa piloto que probó escáneres de armas con inteligencia artificial dentro de algunas estaciones del metro de Nueva York este verano no detectó ningún pasajero con armas de fuego, pero dio alertas falsas más de 100…
Actualizado: Esta publicación ha sido actualizada, ya que el original posiblemente exageró tanto lo que decía el acuerdo de la FTC con respecto a lo que Evolv podría comercializar como la respuesta de Evolv al mismo (lo que sugiere que inte…
Variantes
Una "Variante" es un incidente que comparte los mismos factores causales, produce daños similares e involucra los mismos sistemas inteligentes que un incidente de IA conocido. En lugar de indexar las variantes como incidentes completamente separados, enumeramos las variaciones de los incidentes bajo el primer incidente similar enviado a la base de datos. A diferencia de otros tipos de envío a la base de datos de incidentes, no se requiere que las variantes tengan informes como evidencia externa a la base de datos de incidentes. Obtenga más información del trabajo de investigación.
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