Description: 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.
Entités
Voir toutes les entitésAlleged: Evolv Technology developed an AI system deployed by New York City Government, which harmed New York City subway riders.
Statistiques d'incidents
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
Rapports d'incidents
Chronologie du rapport
NEW YORK (AP) — Un programme pilote testant des scanners d'armes à feu alimentés par l'IA dans certaines stations de métro de New York cet été n'a détecté aucun passager avec des armes à feu - mais a donné de fausses alertes plus de 100 foi…
Mise à jour : Cet article a été mis à jour, car l’original exagérait potentiellement à la fois ce que disait le règlement de la FTC concernant ce qu’Evolv pouvait commercialiser ainsi que la réponse d’Evolv à ce règlement (suggérant qu’elle…
Variantes
Une "Variante" est un incident qui partage les mêmes facteurs de causalité, produit des dommages similaires et implique les mêmes systèmes intelligents qu'un incident d'IA connu. Plutôt que d'indexer les variantes comme des incidents entièrement distincts, nous listons les variations d'incidents sous le premier incident similaire soumis à la base de données. Contrairement aux autres types de soumission à la base de données des incidents, les variantes ne sont pas tenues d'avoir des rapports en preuve externes à la base de données des incidents. En savoir plus sur le document de recherche.
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