Descripción: The VioGén algorithm was designed to help Spanish police assess and prioritize the risk of repeat domestic violence incidents. However, its low-risk assessment of Lobna Hemid reportedly led to inadequate protection; her husband murdered her. Since 2007, 247 women have been killed after being assessed by VioGén. A review of 98 homicides found that 55 of the slain women were scored as negligible or low risk.
Entidades
Ver todas las entidadesAlleged: Spanish law enforcement agencies , Spanish Interior Ministry y VioGén algorithm development team developed an AI system deployed by Spanish law enforcement agencies y Spanish Interior Ministry, which harmed Women in Spain , Stefany González Escarraman , Spanish general public , María , Luz , Lobna Hemid , Eva Jaular y 247 women in Spain (unnamed).
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
In a small apartment outside Madrid on Jan. 11, 2022, an argument over household chores turned violent when Lobna Hemid's husband smashed a wooden shoe rack and used one of the broken pieces to beat her. Her screams were heard by neighbors.…
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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